diff --git a/.github/ISSUE_TEMPLATE/internal-orch-team.yml b/.github/ISSUE_TEMPLATE/internal-orch-team.yml deleted file mode 100644 index 8c4d61df10c..00000000000 --- a/.github/ISSUE_TEMPLATE/internal-orch-team.yml +++ /dev/null @@ -1,49 +0,0 @@ -name: Orchestration team - Request changes to docs -description: File a docs update request that is not already tracked in Orch team's Release Plans (Notion database). -labels: ["content","internal-orch-team"] -body: - - type: markdown - attributes: - value: | - * You can ask questions or submit ideas for the dbt docs in [Issues](https://github.com/dbt-labs/docs-internal/issues/new/choose) - * Before you file an issue read the [Contributing guide](https://github.com/dbt-labs/docs-internal#contributing). - * Check to make sure someone hasn't already opened a similar [issue](https://github.com/dbt-labs/docs-internal/issues). - - - type: checkboxes - id: contributions - attributes: - label: Contributions - description: Please read the contribution docs before opening an issue or pull request. - options: - - label: I have read the contribution docs, and understand what's expected of me. - - - type: textarea - attributes: - label: Link to the page on docs.getdbt.com requiring updates - description: Please link to the page or pages you'd like to see improved. - validations: - required: true - - - type: textarea - attributes: - label: What part(s) of the page would you like to see updated? - description: | - - Give as much detail as you can to help us understand the change you want to see. - - Why should the docs be changed? What use cases does it support? - - What is the expected outcome? - validations: - required: true - - - type: textarea - attributes: - label: Reviewers/Stakeholders/SMEs - description: List the reviewers, stakeholders, and subject matter experts (SMEs) to collaborate with for the docs update. - validations: - required: true - - - type: textarea - attributes: - label: Related Jira tickets - description: Add any other context or screenshots about the feature request here. - validations: - required: false diff --git a/README.md b/README.md index c749fedf95a..d306651f545 100644 --- a/README.md +++ b/README.md @@ -62,18 +62,3 @@ You can click a link available in a Vercel bot PR comment to see and review your Advisory: - If you run into an `fatal error: 'vips/vips8' file not found` error when you run `npm install`, you may need to run `brew install vips`. Warning: this one will take a while -- go ahead and grab some coffee! - -## Running the Cypress tests locally - -Method 1: Utilizing the Cypress GUI -1. `cd` into the repo: `cd docs.getdbt.com` -2. `cd` into the `website` subdirectory: `cd website` -3. Install the required node packages: `npm install` -4. Run `npx cypress open` to open the Cypress GUI, and choose `E2E Testing` as the Testing Type, before finally selecting your browser and clicking `Start E2E testing in {broswer}` -5. Click on a test and watch it run! - -Method 2: Running the Cypress E2E tests headlessly -1. `cd` into the repo: `cd docs.getdbt.com` -2. `cd` into the `website` subdirectory: `cd website` -3. Install the required node packages: `npm install` -4. Run `npx cypress run` diff --git a/contributing/developer-blog.md b/contributing/developer-blog.md deleted file mode 100644 index 0d9b3becba2..00000000000 --- a/contributing/developer-blog.md +++ /dev/null @@ -1,67 +0,0 @@ - -* [Contributing](#contributing) -* [Core Principles](#core-principles) - -## Contributing - -The dbt Developer Blog is a place where analytics practitioners can go to share their knowledge with the community. Analytics Engineering is a discipline we’re all building together. The developer blog exists to cultivate the collective knowledge that exists on how to build and scale effective data teams. - -We currently have editorial capacity for a few Community contributed developer blogs per quarter - if we are oversubscribed we suggest you post on another platform or hold off until the editorial team is ready to take on more posts. - -### What makes a good developer blog post? - -- The short answer: Practical, hands on analytics engineering tutorials and stories - - [Slim CI/CD with Bitbucket](https://docs.getdbt.com/blog/slim-ci-cd-with-bitbucket-pipelines) - - [So You Want to Build a dbt Package](https://docs.getdbt.com/blog/so-you-want-to-build-a-package) - - [Founding an Analytics Engineering Team](https://docs.getdbt.com/blog/founding-an-analytics-engineering-team-smartsheet) -- See the [Developer Blog Core Principles](#core-principles) - -### How do I submit a proposed post? - -To submit a proposed post, open a `Contribute to the dbt Developer Blog` issue on the [Developer Hub repo](https://github.com/dbt-labs/docs.getdbt.com/issues/new/choose). You will be asked for: - -- A short (one paragraph) summary of the post you’d like to publish -- An outline of the post - -You’ll hear back from a member of the dbt Labs teams within 7 days with one of three responses: - -- The post looks good to go as is! We’ll ask you to start creating a draft based off of the initial outline you submitted -- Proposed changes to the outline. This could be additional focus on a topic you mention that’s of high community interest or a tweak to the structure to help with narrative flow -- Not a fit for the developer blog right now. We hugely appreciate *any* interest in submitting to the Developer Blog - right now our biggest backlog is capacity to help folks get these published. See below on how we are thinking about and evaluating potential posts. - -### What is the process once my blog is accepted? - -Once a blog is accepted, we’ll ask you for a date when we can expect the draft by. Typically we’ll ask that you can commit to having this ready within a month of submitting the issue. - -Once you submit a draft, we’ll return a first set of edits within 5 business days. - -The typical turnaround time from issue creation to going live on the developer blog is ~4 to 6 weeks. - -### What happens after my blog is published? - -We’ll share the blog on the dbt Labs social media channels! We also encourage you to share on the dbt Slack in #i-made-this. - -### What if my post doesn’t get approved? - -We want to publish as many community contributors as possible, but not every post will be a fit for the Developer Blog. That’s ok! There are many different reasons why we might not be able to publish a post right now and none of them reflect on the quality of the proposed post. - -- **dbt Labs capacity**: We’re committed to providing hands-on feedback and coaching throughout the process. Our goal is not just to generate great developer blogs - it’s to help build a community of great writers / practitioners who can share their knowledge with the community for years to come. This necessarily means we will be able to take on a lower absolute number of posts in the short term, but will hopefully be helpful for the community long term. -- **Focus on narrative / problem solving - not industry trends**: The developer blog exists, primarily, to tell the stories of analytics engineering practitioners and how they solve problems. The idea is that reading the developer blog gives a feel for what it is like to be a data practitioner on the ground today. This is not a hard and fast rule, but a good way to approach this is “How I/we solved X problem” rather than “How everyone should solve X problem”. - -We are very interested in stacks, new tools and integrations and will happily publish posts about this - with the caveat that the *focus* of the post should be solving real world problems. Hopefully if you are writing about these, this is something that you have used yourself in a hands on, production implementation. - -- **Right sized scope**: We want to be able to cover a topic in-depth and dig into the nuances. Big topics like “How should you structure your data team” or “How to ensure data quality in your organization” will be tough to cover in the scope of a single post. If you have a big idea - try subdividing it! “How should you structure your data team” could become “How we successfully partnered with our RevOps team on improving lead tracking” and “How to ensure data quality in your organization” might be “How we cleaned up our utm tracking”. - -### What if I need help / have questions: - -- Feel free to post any questions in #community-writers on the dbt Slack. - -## Core Principles - -- 🧑🏻‍🤝‍🧑🏾 The dbt Developer blog is written by humans **- individual analytics professionals sharing their insight with the world. To the extent feasible, a community member posting on the developer blog is not staking an official organizational stance, but something that *they* have learned or believe based on their work. This is true for dbt Labs employees as well. -- 💍 Developer blog content is knowledge rich - these are posts that readers share, bookmark and come back to time and time again. -- ⛹🏼‍♂️ Developer blog content is written by and for *practitioners* - end users of analytics tools (and sometimes people that work with practitioners). -- ⭐ Developer blog content is best when it is *the story which the author is uniquely positioned to tell.* Authors are encouraged to consider what insight they have that is specific to them and the work they have done. -- 🏎️ Developer blog content is actionable - readers walk away with a clear sense of how they can use this information to be a more effective practitioner. Posts include code snippets, Loom walkthroughs and hands-on, practical information that can be integrated into daily workflows. -- 🤏 Nothing is too small to share - what you think is simple has the potential to change someone's week. -- 🔮 Developer blog content is present focused —posts tell a story of a thing that you've already done or are actively doing, not something that you may do in the future. diff --git a/website/blog/2021-11-23-how-to-upgrade-dbt-versions.md b/website/blog/2021-11-23-how-to-upgrade-dbt-versions.md index f7e5786bc70..0b1f1fe26bd 100644 --- a/website/blog/2021-11-23-how-to-upgrade-dbt-versions.md +++ b/website/blog/2021-11-23-how-to-upgrade-dbt-versions.md @@ -17,8 +17,9 @@ is_featured: true It's been a few years since dbt-core turned 1.0! Since then, we've committed to releasing zero breaking changes whenever possible and it's become much easier to upgrade dbt Core versions. In 2024, we're taking this promise further by: + - Stabilizing interfaces for everyone — adapter maintainers, metadata consumers, and (of course) people writing dbt code everywhere — as discussed in [our November 2023 roadmap update](https://github.com/dbt-labs/dbt-core/blob/main/docs/roadmap/2023-11-dbt-tng.md). -- Introducing **Versionless** in dbt Cloud. No more manual upgrades and no more need for _a second sandbox project_ just to try out new features in development. For more details, refer to [Upgrade Core version in Cloud](/docs/dbt-versions/upgrade-dbt-version-in-cloud). +- Introducing [Release tracks](/docs/dbt-versions/cloud-release-tracks) (formerly known as Versionless) to dbt Cloud. No more manual upgrades and no need for _a second sandbox project_ just to try out new features in development. For more details, refer to [Upgrade Core version in Cloud](/docs/dbt-versions/upgrade-dbt-version-in-cloud). We're leaving the rest of this post as is, so we can all remember how it used to be. Enjoy a stroll down memory lane. diff --git a/website/blog/2024-04-22-extended-attributes.md b/website/blog/2024-04-22-extended-attributes.md index 18d4ff0b64c..57636cc8f6b 100644 --- a/website/blog/2024-04-22-extended-attributes.md +++ b/website/blog/2024-04-22-extended-attributes.md @@ -80,7 +80,7 @@ All you need to do is configure an environment as staging and enable the **Defer ## Upgrading on a curve -Lastly, let’s consider a more specialized use case. Imagine we have a "tiger team" (consisting of a lone analytics engineer named Dave) tasked with upgrading from dbt version 1.6 to the new **Versionless** setting, to take advantage of added stability and feature access. We want to keep the rest of the data team being productive in dbt 1.6 for the time being, while enabling Dave to upgrade and do his work in the new versionless mode. +Lastly, let’s consider a more specialized use case. Imagine we have a "tiger team" (consisting of a lone analytics engineer named Dave) tasked with upgrading from dbt version 1.6 to the new **[Latest release track](/docs/dbt-versions/cloud-release-tracks)**, to take advantage of new features and performance improvements. We want to keep the rest of the data team being productive in dbt 1.6 for the time being, while enabling Dave to upgrade and do his work with Latest (and greatest) dbt. ### Development environment diff --git a/website/blog/2024-05-22-latest-dbt-stability-improvement-innovation.md b/website/blog/2024-05-22-latest-dbt-stability-improvement-innovation.md index 078dab198fa..f2c25f3da8c 100644 --- a/website/blog/2024-05-22-latest-dbt-stability-improvement-innovation.md +++ b/website/blog/2024-05-22-latest-dbt-stability-improvement-innovation.md @@ -1,5 +1,5 @@ --- -title: "How we're making sure you can confidently go \"Versionless\" in dbt Cloud" +title: "How we're making sure you can confidently switch to the \"Latest\" release track in dbt Cloud" description: "Over the past 6 months, we've laid a stable foundation for continuously improving dbt." slug: latest-dbt-stability @@ -12,23 +12,27 @@ date: 2024-05-02 is_featured: true --- +import Latest from '/snippets/_release-stages-from-versionless.md' + + + As long as dbt Cloud has existed, it has required users to select a version of dbt Core to use under the hood in their jobs and environments. This made sense in the earliest days, when dbt Core minor versions often included breaking changes. It provided a clear way for everyone to know which version of the underlying runtime they were getting. However, this came at a cost. While bumping a project's dbt version *appeared* as simple as selecting from a dropdown, there was real effort required to test the compatibility of the new version against existing projects, package dependencies, and adapters. On the other hand, putting this off meant foregoing access to new features and bug fixes in dbt. -But no more. Today, we're ready to announce the general availability of a new option in dbt Cloud: [**"Versionless."**](https://docs.getdbt.com/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) +But no more. Today, we're ready to announce the general availability of a new option in dbt Cloud: [**the "Latest" release track.**](/docs/dbt-versions/cloud-release-tracks) For customers, this means less maintenance overhead, faster access to bug fixes and features, and more time to focus on what matters most: building trusted data products. This will be our stable foundation for improvement and innovation in dbt Cloud. -But we wanted to go a step beyond just making this option available to you. In this blog post, we aim to shed a little light on the extensive work we've done to ensure that using "Versionless" is a stable, reliable experience for the thousands of customers who rely daily on dbt Cloud. +But we wanted to go a step beyond just making this option available to you. In this blog post, we aim to shed a little light on the extensive work we've done to ensure that using the "Latest" release track is a stable and reliable experience for the thousands of customers who rely daily on dbt Cloud. ## How we safely deploy dbt upgrades to Cloud We've put in place a rigorous, best-in-class suite of tests and control mechanisms to ensure that all changes to dbt under the hood are fully vetted before they're deployed to customers of dbt Cloud. -This pipeline has in fact been in place since January! It's how we've already been shipping continuous changes to the hundreds of customers who've selected "Versionless" while it's been in Beta and Preview. In that time, this process has enabled us to prevent multiple regressions before they were rolled out to any customers. +This pipeline has in fact been in place since January! It's how we've already been shipping continuous changes to the hundreds of customers who've selected the "Latest" release track while it's been in Beta and Preview. In that time, this process has enabled us to prevent multiple regressions before they were rolled out to any customers. We're very confident in the robustness of this process**. We also know that we'll need to continue building trust with time.** We're sharing details about this work in the spirit of transparency and to build that trust. @@ -82,9 +86,9 @@ All incidents are retrospected to make sure we not only identify and fix the roo ::: -The outcome of this process is that, when you select "Versionless" in dbt Cloud, the time between an improvement being made to dbt Core and you *safely* getting access to it in your projects is a matter of days — rather than months of waiting for the next dbt Core release, on top of any additional time it may have taken to actually carry out the upgrade. +The outcome of this process is that, when you select the "Latest" release track in dbt Cloud, the time between an improvement being made to dbt Core and you *safely* getting access to it in your projects is a matter of days — rather than months of waiting for the next dbt Core release, on top of any additional time it may have taken to actually carry out the upgrade. -We’re pleased to say that since the beta launch of “Versionless” in dbt Cloud in March, **we have not had any functional regressions reach customers**, while we’ve also been shipping multiple improvements to dbt functionality every day. This is a foundation that we aim to build on for the foreseeable future. +We’re pleased to say that, at the time of writing (May 2, 2024), since the beta launch of the "Latest" release track in dbt Cloud in March, **we have not had any functional regressions reach customers**, while we’ve also been shipping multiple improvements to dbt functionality every day. This is a foundation that we aim to build on for the foreseeable future. ## Stability as a feature @@ -98,7 +102,7 @@ The adapter interface — i.e. how dbt Core actually connects to a third-party d To solve that, we've released a new set of interfaces that are entirely independent of the `dbt-core` library: [`dbt-adapters==1.0.0`](https://github.com/dbt-labs/dbt-adapters). From now on, any changes to `dbt-adapters` will be backward and forward-compatible. This also decouples adapter maintenance from the regular release cadence of dbt Core — meaning maintainers get full control over when they ship implementations of new adapter-powered features. -Note that adapters running in dbt Cloud **must** be [migrated to the new decoupled architecture](https://github.com/dbt-labs/dbt-adapters/discussions/87) as a baseline in order to support the new "Versionless" option. +Note that adapters running in dbt Cloud **must** be [migrated to the new decoupled architecture](https://github.com/dbt-labs/dbt-adapters/discussions/87) as a baseline in order to support the new "Latest" release track. ### Managing behavior changes: stability as a feature @@ -118,7 +122,7 @@ We’ve now [formalized our development best practices](https://github.com/dbt-l In conclusion, we’re putting a lot of new muscle behind our commitments to dbt Cloud customers, the dbt Community, and the broader ecosystem: -- **Continuous updates**: "Versionless" dbt Cloud simplifies the update process, ensuring you always have the latest features and bug fixes without the maintenance overhead. +- **Continuous updates**: The "Latest" release track in dbt Cloud simplifies the update process, ensuring you always have the latest features and bug fixes without the maintenance overhead. - **A rigorous new testing and deployment process**: Our new testing pipeline ensures that every update is carefully vetted against documented interfaces, Cloud-supported adapters, and popular packages before it reaches you. This process minimizes the risk of regressions — and has now been successful at entirely preventing them for hundreds of customers over multiple months. - **A commitment to stability**: We’ve reworked our approaches to adapter interfaces, behaviour change management, and metadata artifacts to give you more stability and control. diff --git a/website/blog/2024-06-12-putting-your-dag-on-the-internet.md b/website/blog/2024-06-12-putting-your-dag-on-the-internet.md index 535cfc34d6e..54864916d0e 100644 --- a/website/blog/2024-06-12-putting-your-dag-on-the-internet.md +++ b/website/blog/2024-06-12-putting-your-dag-on-the-internet.md @@ -12,7 +12,7 @@ date: 2024-06-14 is_featured: true --- -**New in dbt: allow Snowflake Python models to access the internet** +## New in dbt: allow Snowflake Python models to access the internet With dbt 1.8, dbt released support for Snowflake’s [external access integrations](https://docs.snowflake.com/en/developer-guide/external-network-access/external-network-access-overview) further enabling the use of dbt + AI to enrich your data. This allows querying of external APIs within dbt Python models, a functionality that was required for dbt Cloud customer, [EQT AB](https://eqtgroup.com/). Learn about why they needed it and how they helped build the feature and get it shipped! @@ -45,7 +45,7 @@ This API is open and if it requires an API key, handle it similarly to managing For simplicity’s sake, we will show how to create them using [pre-hooks](/reference/resource-configs/pre-hook-post-hook) in a model configuration yml file: -``` +```yml models: - name: external_access_sample config: @@ -57,7 +57,7 @@ models: Then we can simply use the new external_access_integrations configuration parameter to use our network rule within a Python model (called external_access_sample.py): -``` +```python import snowflake.snowpark as snowpark def model(dbt, session: snowpark.Session): dbt.config( @@ -75,7 +75,7 @@ def model(dbt, session: snowpark.Session): The result is a model with some json I can parse, for example, in a SQL model to extract some information: -``` +```sql {{ config( materialized='incremental', @@ -108,12 +108,12 @@ The result is a model that will keep track of dbt invocations, and the current U This is a very new area to Snowflake and dbt -- something special about SQL and dbt is that it’s very resistant to external entropy. The second we rely on API calls, Python packages and other external dependencies, we open up to a lot more external entropy. APIs will change, break, and your models could fail. -Traditionally dbt is the T in ELT (dbt overview [here](https://docs.getdbt.com/terms/elt)), and this functionality unlocks brand new EL capabilities for which best practices do not yet exist. What’s clear is that EL workloads should be separated from T workloads, perhaps in a different modeling layer. Note also that unless using incremental models, your historical data can easily be deleted. dbt has seen a lot of use cases for this, including this AI example as outlined in this external [engineering blog post](https://klimmy.hashnode.dev/enhancing-your-dbt-project-with-large-language-models). +Traditionally dbt is the T in ELT (dbt overview [here](https://docs.getdbt.com/terms/elt)), and this functionality unlocks brand new EL capabilities for which best practices do not yet exist. What’s clear is that EL workloads should be separated from T workloads, perhaps in a different modeling layer. Note also that unless using incremental models, your historical data can easily be deleted. dbt has seen a lot of use cases for this, including this AI example as outlined in this external [engineering blog post](https://klimmy.hashnode.dev/enhancing-your-dbt-project-with-large-language-models). -**A few words about the power of Commercial Open Source Software** +## A few words about the power of Commercial Open Source Software In order to get this functionality shipped quickly, EQT opened a pull request, Snowflake helped with some problems we had with CI and a member of dbt Labs helped write the tests and merge the code in! -dbt now features this functionality in dbt 1.8+ or the “Versionless” option of dbt Cloud (dbt overview [here](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless)). +dbt now features this functionality in dbt 1.8+ and all [Release tracks](/docs/dbt-versions/cloud-release-tracks) in dbt Cloud. dbt Labs staff and community members would love to chat more about it in the [#db-snowflake](https://getdbt.slack.com/archives/CJN7XRF1B) slack channel. diff --git a/website/blog/2024-10-04-hybrid-mesh.md b/website/blog/2024-10-04-hybrid-mesh.md index 34b2a67d1cb..05a45599318 100644 --- a/website/blog/2024-10-04-hybrid-mesh.md +++ b/website/blog/2024-10-04-hybrid-mesh.md @@ -59,7 +59,7 @@ This allows dbt Cloud to know about the contents and metadata of your project, w - Note: If you have [environment variables](/docs/build/environment-variables) in your project, dbt Cloud environment variables must be prefixed with `DBT_ `(including `DBT_ENV_CUSTOM_ENV_` or `DBT_ENV_SECRET`). Follow the instructions in [this guide](https://docs.getdbt.com/guides/core-to-cloud-1?step=8#environment-variables) to convert them for dbt Cloud. - Each upstream Core project has to have a production [environment](/docs/dbt-cloud-environments) in dbt Cloud. You need to configure credentials and environment variables in dbt Cloud just so that it will resolve relation names to the same places where your dbt Core workflows are deploying those models. - Set up a [merge job](/docs/deploy/merge-jobs) in a production environment to run `dbt parse`. This will enable connecting downstream projects in dbt Mesh by producing the necessary [artifacts](/reference/artifacts/dbt-artifacts) for cross-project referencing. - - Note: Set up a regular job to run `dbt build` instead of using a merge job for `dbt parse`, and centralize your dbt orchestration by moving production runs to dbt Cloud. Check out [this guide](/guides/core-to-cloud-1?step=9) for more details on converting your production runs to dbt Cloud. + - Optional: Set up a regular job to run `dbt build` instead of using a merge job for `dbt parse`, and centralize your dbt orchestration by moving production runs to dbt Cloud. Check out [this guide](/guides/core-to-cloud-1?step=9) for more details on converting your production runs to dbt Cloud. - Optional: Set up a regular job (for example, daily) to run `source freshness` and `docs generate`. This will hydrate dbt Cloud with additional metadata and enable features in [dbt Explorer](/docs/collaborate/explore-projects) that will benefit both teams, including [Column-level lineage](/docs/collaborate/column-level-lineage). ### Step 3: Create and connect your downstream projects to your Core project using dbt Mesh diff --git a/website/blog/2024-11-04-test-smarter-not-harder.md b/website/blog/2024-11-04-test-smarter-not-harder.md new file mode 100644 index 00000000000..58adfb38cb9 --- /dev/null +++ b/website/blog/2024-11-04-test-smarter-not-harder.md @@ -0,0 +1,163 @@ +--- +title: "Test smarter not harder: add the right tests to your dbt project" +description: "Testing your data should drive action, not accumulate alerts. We synthesized countless customer experiences to build a repeatable testing framework." +slug: test-smarter-not-harder + +authors: [faith_mckenna, jerrie_kumalah_kenney] + +tags: [analytics craft] +hide_table_of_contents: false + +date: 2024-11-11 +is_featured: true +--- + + + +The [Analytics Development Lifecycle (ADLC)](https://www.getdbt.com/resources/guides/the-analytics-development-lifecycle) is a workflow for improving data maturity and velocity. Testing is a key phase here. Many dbt developers tend to focus on [primary keys and source freshness.](https://www.getdbt.com/blog/building-a-data-quality-framework-with-dbt-and-dbt-cloud) We think there is a more holistic and in-depth path to tread. Testing is a key piece of the ADLC, and it should drive data quality. + +In this blog, we’ll walk through a plan to define data quality. This will look like: + +- identifying *data hygiene* issues +- identifying *business-focused anomaly* issues +- identifying *stats-focused anomaly* issues + +Once we have *defined* data quality, we’ll move on to *prioritize* those concerns. We will: + +- think through each concern in terms of the breadth of impact +- decide if each concern should be at error or warning severity + + + +### Who are we? + +Let’s start with introductions - we’re Faith and Jerrie, and we work on dbt Labs’s training and services teams, respectively. By working closely with countless companies using dbt, we’ve gained unique perspectives of the landscape. + +The training team collates problems organizations think about today and gauge how our solutions fit. These are shorter engagements, which means we see the data world shift and change in real time. Resident Architects spend much more time with teams to craft much more in-depth solutions, figure out where those solutions are helping, and where problems still need to be addressed. Trainers help identify patterns in the problems data teams face, and Resident Architects dive deep on solutions. + +Today, we’ll guide you through a particularly thorny problem: testing. + +## Why testing? + +Mariah Rogers broke early ground on data quality and testing in her [Coalesce 2022 talk](https://www.youtube.com/watch?v=hxvVhmhWRJA). We’ve seen similar talks again at Coalesce 2024, like [this one](https://www.youtube.com/watch?v=iCG-5vqMRAo) from the data team at Aiven and [this one](https://www.youtube.com/watch?v=5bRG3y9IM4Q&list=PL0QYlrC86xQnWJ72sJlzDqPS0peE7j9Ed&index=71) from the co-founder at Omni Analytics. These talks share a common theme: testing your dbt project too much can get out of control quickly, leading to alert fatigue. + +In our customer engagements, we see *wildly different approaches* to testing data. We’ve definitely seen what Mariah, the Aiven team, and the Omni team have described, which is so many tests that errors and alerts just become noise. We’ve also seen the opposite end of the spectrum—only primary keys being tested. From our field experiences, we believe there’s room for a middle path. +A desire for a better approach to data quality and testing isn’t just anecdotal to Coalesce, or to dbt’s training and services. The dbt community has long called for a more intentional approach to data quality and testing - data quality is on the industry’s mind! In fact, [57% of respondents](https://www.getdbt.com/resources/reports/state-of-analytics-engineering-2024) to dbt’s 2024 State of Analytics Engineering survey said that data quality is a predominant issue facing their day-to-day work. + +### What does d@tA qUaL1Ty even mean?! + +High-quality data is *trusted* and *used frequently.* It doesn’t get argued over or endlessly scrutinized for matching to other data. Data *testing* should lead to higher data *quality* and insights, period. + +Best practices in data quality are still nascent. That said, a lot of important baseline work has been done here. There are [case](https://medium.com/@AtheonAnalytics/mastering-data-testing-with-dbt-part-1-689b2a025675) [studies](https://medium.com/@AtheonAnalytics/mastering-data-testing-with-dbt-part-2-c4031af3df18) on implementing dbt testing well. dbt Labs also has an [Advanced Testing](https://learn.getdbt.com/courses/advanced-testing) course, emphasizing that testing should spur action and be focused and informative enough to help address failures. You can even enforce testing best practices and dbt Labs’s own best practices using the [dbt_meta_testing](https://hub.getdbt.com/tnightengale/dbt_meta_testing/latest/) or [dbt_project_evaluator](https://github.com/dbt-labs/dbt-project-evaluator) packages and dbt Explorer’s [Recommendations](https://docs.getdbt.com/docs/collaborate/project-recommendations) page. + +The missing piece is still cohesion and guidance for everyday practitioners to help develop their testing framework. + +To recap, we’re going to start with: + +- identifying *data hygiene* issues +- identifying *business-focused anomaly* issues +- identifying *stats-focused anomaly* issues + +Next, we’ll prioritize. We will: + +- think through each concern in terms of the breadth of impact +- decide if each concern should be at error or warning severity + +Get a pen and paper (or a google doc) and join us in constructing your own testing framework. + +## Identifying data quality issues in your pipeline + +Let’s start our framework by *identifying* types of data quality issues. + +In our daily work with customers, we find that data quality issues tend to fall into one of three broad buckets: *data hygiene, business-focused anomalies,* and *stats-focused anomalies.* Read the bucket descriptions below, and list 2-3 data quality concerns in your own business context that fall into each bucket. + +### Bucket 1: Data hygiene + +*Data hygiene* issues are concerns you address in your [staging layer.](https://docs.getdbt.com/best-practices/how-we-structure/2-staging) Hygienic data meets your expectations around formatting, completeness, and granularity requirements. Here are a few examples. + +- *Granularity:* primary keys are unique and not null. Duplicates throw off calculations. +- *Completeness:* columns that should always contain text, *do.* Incomplete data often has to get excluded, reducing your overall analytical power. +- *Formatting:* email addresses always have a valid domain. Incorrect emails may affect things like marketing outreach. + +### Bucket 2: Business-focused anomalies + +*Business-focused anomalies* catch unexpected behavior. You can flag unexpected behavior by clearly defining *expected* behavior. *Business-focused anomalies* are when aspects of the data differ from what you know to be typical in your business. You’ll know what’s typical either through your own analyses, your colleagues’ analyses, or things your stakeholder homies point out to you. + +Since business-focused anomaly testing is set by a human, it will be fluid and need to be adjusted periodically. Here’s an example. + +Imagine you’re a sales analyst. Generally, you know that if your daily sales amount goes up or down by more than 20% daily, that’s bad. Specifically, it’s usually a warning sign for fraud or the order management system (OMS) dropping orders. You set a test in dbt to fail if any given day’s sales amount is a delta of 20% from the previous day. This works for a while. + +Then, you have a stretch of 3 months where your test fails 5 times a week! Every time you investigate, it turns out to be valid consumer behavior. You’re suddenly in hypergrowth, and sales are legitimately increasing that much. + +Your 20%-change fraud and OMS failure detector is no longer valid. You need to investigate anew which sales spikes or drops indicate fraud or OMS problems. Once you figure out a new threshold, you’ll go back and adjust your testing criteria. + +Although your data’s expected behavior will shift over time, you should still commit to defining business-focused anomalies to grow your understanding of what is normal for your data. + +Here’s how to identify potential anomalies. + +Start at your business intelligence (BI) layer. Pick 1-3 dashboards or tables that you *know* are used frequently. List these 1-3 dashboards or tables. For each dashboard or table you have, identify 1-3 “expected” behaviors that your end-users rely on. Here are a few examples to get you thinking: + +- Revenue numbers should not change by more than X% in Y amount of time. This could indicate fraud or OMS problems. +- Monthly active users should not decline more than X% after the initial onboarding period. This might indicate user dissatisfaction, usability issues, or that users not finding a feature valuable. +- Exam passing rates should stay above Y%. A decline below that threshold may indicate recent content changes or technical issues are affecting understanding or accessibility. + +You should also consider what data issues you have had in the past! Look through recent data incidents and pick out 3 or 4 to guard against next time. These might be in a #data-questions channel or perhaps a DM from a stakeholder. + +### Bucket 3: Stats-focused anomalies + +*Stats-focused anomalies* are fluctuations that go against your expected volumes or metrics. Some examples include: + +- Volume anomalies. This could be site traffic amounts that may indicate illicit behavior, or perhaps site traffic dropping one day then doubling the next, indicating that a chunk of data were not loaded properly. +- Dimensional anomalies, like too many product types underneath a particular product line that may indicate incorrect barcodes. +- Column anomalies, like sale values more than a certain number of standard deviations from a mean, that may indicate improper discounting. + +Overall, stats-focused anomalies can indicate system flaws, illicit site behavior, or fraud, depending on your industry. They also tend to require more advanced testing practices than we are covering in this blog. We feel stats-based anomalies are worth exploring once you have a good handle on your data hygiene and business-focused anomalies. We won’t give recommendations on stats-focused anomalies in this post. + +## How to prioritize data quality concerns in your pipeline + +Now, you have a written and categorized list of data hygiene concerns and business-focused anomalies to guard against. It’s time to *prioritize* which quality issues deserve to fail your pipelines. + +To prioritize your data quality concerns, think about real-life impact. A couple of guiding questions to consider are: + +- Are your numbers *customer-facing?* For example, maybe you work with temperature-tracking devices. Your customers rely on these devices to show them average temperatures on perishable goods like strawberries in-transit. What happens if the temperature of the strawberries reads as 300C when they know their refrigerated truck was working just fine? How is your brand perception impacted when the numbers are wrong? +- Are your numbers *used to make financial decisions?* For example, is the marketing team relying on your numbers to choose how to spend campaign funds? +- Are your numbers *executive-facing?* Will executives use these numbers to reallocate funds or shift priorities? + +We think these 3 categories above constitute high-impact, pipeline-failing events, and should be your top priorities. Of course, adjust priority order if your business context calls for it. + +Consult your list of data quality issues in the categories we mention above. Decide and mark if any are customer facing, used for financial decisions, or are executive-facing. Mark any data quality issues in those categories as “error”. These are your pipeline-failing events. + +If any data quality concerns fall outside of these 3 categories, we classify them as **nice-to-knows**. **Nice-to-know** data quality testing *can* be helpful. But if you don’t have a *specific action you can immediately take* when a nice-to-know quality test fails, the test *should be a warning, not an error.* + +You could also remove nice-to-know tests altogether. Data testing should drive action. The more alerts you have in your pipeline, the less action you will take. Configure alerts with care! + +However, we do think nice-to-know tests are worth keeping *if and only if* you are gathering evidence for action you plan to take within the next 6 months, like product feature research. In a scenario like that, those tests should still be set to warning. + +### Start your action plan + +Now, your data quality concerns are listed and prioritized. Next, add 1 or 2 initial debugging steps you will take if/when the issues surface. These steps should get added to your framework document. Additionally, consider adding them to a [test’s description.](https://discourse.getdbt.com/t/is-it-possible-to-add-a-description-to-singular-tests/5472/4) + +This step is *important.* Data quality testing should spur action, not accumulate alerts. Listing initial debugging steps for each concern will refine your list to the most critical elements. + +If you can't identify an action step for any quality issue, *remove it*. Put it on a backlog and research what you can do when it surfaces later. + +Here’s a few examples from our list of unexpected behaviors above. + +- For calculated field X, a value above Y or below Z is not possible. + - *Debugging initial steps* + - Use dbt test SQL or recent test results in dbt Explorer to find problematic rows + - Check these rows in staging and first transformed model + - Pinpoint where unusual values first appear +- Revenue shouldn’t change by more than X% in Y amount of time. + - *Debugging initial steps:* + - Check recent revenue values in staging model + - Identify transactions near min/max values + - Discuss outliers with sales ops team + +You now have written out a prioritized list of data quality concerns, as well as action steps to take when each concern surfaces. Next, consult [hub.getdbt.com](http://hub.getdbt.com) and find tests that address each of your highest priority concerns. [dbt-expectations](https://hub.getdbt.com/calogica/dbt_expectations/latest/) and [dbt_utils](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/) are great places to start. + +The data tests you’ve marked as “errors” above should get error-level severity. Any concerns falling into that nice-to-know category should either *not get tested* or have their tests *set to warning.* + +Your data quality priorities list is a living reference document. We recommend linking it in your project’s README so that you can go back and edit it as your testing needs evolve. Additionally, developers in your project should have easy access to this document. Maintaining good data quality is everyone’s responsibility! + +As you try these ideas out, come to the dbt Community Slack and let us know what works and what doesn’t. Data is a community of practice, and we are eager to hear what comes out of yours. diff --git a/website/blog/2024-11-27-test-smarter-part-2.md b/website/blog/2024-11-27-test-smarter-part-2.md new file mode 100644 index 00000000000..4fabe066011 --- /dev/null +++ b/website/blog/2024-11-27-test-smarter-part-2.md @@ -0,0 +1,125 @@ +--- +title: "Test smarter not harder: Where should tests go in your pipeline?" +description: "Testing your data should drive action, not accumulate alerts. We take our testing framework developed in our last post and make recommendations for where tests ought to go at each transformation stage." +slug: test-smarter-where-tests-should-go + +authors: [faith_mckenna, jerrie_kumalah_kenney] + +tags: [analytics craft] +hide_table_of_contents: false + +date: 2024-12-09 +is_featured: true +--- + +👋 Greetings, dbt’ers! It’s Faith & Jerrie, back again to offer tactical advice on *where* to put tests in your pipeline. + +In [our first post](/blog/test-smarter-not-harder) on refining testing best practices, we developed a prioritized list of data quality concerns. We also documented first steps for debugging each concern. This post will guide you on where specific tests should go in your data pipeline. + +*Note that we are constructing this guidance based on how we [structure data at dbt Labs.](/best-practices/how-we-structure/1-guide-overview#guide-structure-overview)* You may use a different modeling approach—that’s okay! Translate our guidance to your data’s shape, and let us know in the comments section what modifications you made. + +First, here’s our opinions on where specific tests should go: + +- Source tests should be fixable data quality concerns. See the [callout box below](#sources) for what we mean by “fixable”. +- Staging tests should be business-focused anomalies specific to individual tables, such as accepted ranges or ensuring sequential values. In addition to these tests, your staging layer should clean up any nulls, duplicates, or outliers that you can’t fix in your source system. You generally don’t need to test your cleanup efforts. +- Intermediate and marts layer tests should be business-focused anomalies resulting specifically from joins or calculations. You also may consider adding additional primary key and not null tests on columns where it’s especially important to protect the grain. + + + +## Where should tests go in your pipeline? + +![A horizontal, multicolored diagram that shows examples of where tests ought to be placed in a data pipeline.](/img/blog/2024-11-27-test-smarter-part-2/testing_pipeline.png) + +This diagram above outlines where you might put specific data tests in your pipeline. Let’s expand on it and discuss where each type of data quality issue should be tested. + +### Sources + +Tests applied to your sources should indicate *fixable-at-the-source-system* issues. If your source tests flag source system issues that aren’t fixable, remove the test and mitigate the problem in your staging layer instead. + +:::tip[What does fixable mean?] +We consider a "fixable-at-the-source-system" issue to be something that: + +- You yourself can fix in the source system. +- You know the right person to fix it and have a good enough relationship with them that you know you can *get it fixed.* + +You may have issues that can *technically* get fixed at the source, but it won't happen till the next planning cycle, or you need to develop better relationships to get the issue fixed, or something similar. This demands a more nuanced approach than we'll cover in this post. If you have thoughts on this type of situation, let us know! + +::: + +Here’s our recommendation for what tests belong on your sources. + +- Source freshness: testing data freshness for sources that are critical to your pipelines. + - If any sources feed into any of the “top 3” [priority categories](https://docs.getdbt.com/blog/test-smarter-not-harder#how-to-prioritize-data-quality-concerns-in-your-pipeline) in our last post, use [`dbt source freshness`](https://docs.getdbt.com/docs/deploy/source-freshness) in your job execution commands and set the severity to `error`. That way, if source freshness fails, so does your job. + - If none of your sources feed into high priority categories, set your source freshness severity to `warn` and add source freshness to your job execution commands. That way, you still get source freshness information but stale data won't fail your pipeline. +- Data hygiene: tests that are *fixable* in the source system (see our note above on “fixability”). + - Examples: + - Duplicate customer records that can be deleted in the source system + - Null records, such as a customer name or email address, that can be entered into the source system + - Primary key testing where duplicates are removable in the source system + +### Staging + +In the staging layer, your models should be cleaning up or mitigating data issues that can't be fixed at the source. Your tests should be focused on business anomaly detection. + +- Data cleanup and issue mitigation: Use our [best practices around staging layers](https://docs.getdbt.com/best-practices/how-we-structure/2-staging) to clean things up. Don’t add tests to your cleanup efforts. If you’re filtering out nulls in a column, adding a not_null test is repetitive! 🌶️ +- Business-focused anomaly examples: these are data quality issues you *should* test for in your staging layer, because they fall outside of your business’s defined norms. These might be: + - Values inside a single column that fall outside of an acceptable range. For example, a store selling a greater quantity of limited-edition items than they received in their stock delivery. + - Values that should always be positive, are positive. This might look like a negative transaction amount that isn’t classified as a return. This failing test would then spur further investigation into the offending transaction. + - An unexpected uptick in volume of a quantity column beyond a pre-defined percentage. This might look like a store’s customer volume spiking unexpectedly and outside of expected seasonal norms. This is an anomaly that could indicate a bug or modeling issue. + +### Intermediate (if applicable) + +In your intermediate layer, focus on data hygiene and anomaly tests for new columns. Don’t re-test passthrough columns from sources or staging. Here are some examples of tests you might put in your intermediate layer based on the use cases of intermediate models we [outline in this guide](/best-practices/how-we-structure/3-intermediate#intermediate-models). + +- Intermediate models often re-grain models to prepare them for marts. + - Add a primary key test to any re-grained models. + - Additionally, consider adding a primary key test to models where the grain *has remained the same* but has been *enriched.* This helps future-proof your enriched models against future developers who may not be able to glean your intention from SQL alone. +- Intermediate models may perform a first set of joins or aggregations to reduce complexity in a final mart. + - Add simple anomaly tests to verify the behavior of your sets of joins and aggregations. This may look like: + - An [accepted_values](/reference/resource-properties/data-tests#accepted_values) test on a newly calculated categorical column. + - A [mutually_exclusive_ranges](https://github.com/dbt-labs/dbt-utils#mutually_exclusive_ranges-source) test on two columns whose values behave in relation to one another (ex: asserting age ranges do not overlap). + - A [not_constant](https://github.com/dbt-labs/dbt-utils#not_constant-source) test on a column whose value should be continually changing (ex: page view counts on website analytics). +- Intermediate models may isolate complex operations. + - The anomaly tests we list above may suffice here. + - You might also consider [unit testing](/docs/build/unit-tests) any particularly complex pieces of SQL logic. + +### Marts + +Marts layer testing will follow the same hygiene-or-anomaly pattern as staging and intermediate. Similar to your intermediate layer, you should focus your testing on net-new columns in your marts layer. This might look like: + +- Unit tests: validate especially complex transformation logic. For example: + - Calculating dates in a way that feeds into forecasting. + - Customer segmentation logic, especially logic that has a lot of CASE-WHEN statements. +- Primary key tests: focus on where where your mart's granularity has changed from its staging/intermediate inputs. + - Similar to the intermediate models above, you may also want to add primary key tests to models whose grain hasn’t changed, but have been enriched with other data. Primary key tests here communicate your intent. +- Business focused anomaly tests: focus on *new* calculated fields, such as: + - Singular tests on high-priority, high-impact tables where you have a specific problem you want forewarning about. + - This might be something like fuzzy matching logic to detect when the same person is making multiple emails to extend a free trial beyond its acceptable end date. + - A test for calculated numerical fields that shouldn’t vary by more than certain percentage in a week. + - A calculated ledger table that follows certain business rules, i.e. today’s running total of spend must always be greater than yesterday’s. + +### CI/CD + +All of the testing you’ve applied in your different layers is the manual work of constructing your framework. CI/CD is where it gets automated. + +You should run a [slim CI](/best-practices/best-practice-workflows#run-only-modified-models-to-test-changes-slim-ci) to optimize your resource consumption. + +With CI/CD and your regular production runs, your testing framework can be on autopilot. 😎 + +If and when you encounter failures, consult your trusty testing framework doc you built in our [earlier post](/blog/test-smarter-not-harder). + +### Advanced CI + +In the early stages of your smarter testing journey, start with dbt Cloud’s built-in flags for [advanced CI](/docs/deploy/advanced-ci). In PRs with advanced CI enabled, dbt Cloud will flag what has been modified, added, or removed in the “compare changes” section. These three flags offer confidence and evidence that your changes are what you expect. Then, hand them off for peer review. Advanced CI helps jump start your colleague’s review of your work by bringing all of the implications of the change into one place. + +We consider usage of Advanced CI beyond the modified, added, or changed gut checks to be an advanced (heh) testing strategy, and look forward to hearing how you use it. + +## Wrapping it all up + +Judicious data testing is like training for a marathon. It’s not productive to go run 20 miles a day and hope that you’ll be marathon-ready and uninjured. Similarly, throwing data tests randomly at your data pipeline without careful thought is not going to tell you much about your data quality. + +Runners go into marathons with training plans. Analytics engineers who care about data quality approach the issue with a plan, too. + +As you try out some of the guidance above here, remember that your testing needs are going to evolve over time. Don’t be afraid to revise your original testing strategy. + +Let us know your thoughts on these strategies in the comments section. Try them out, and share your thoughts to help us refine them. diff --git a/website/blog/authors.yml b/website/blog/authors.yml index 271130a477d..3070ec806b5 100644 --- a/website/blog/authors.yml +++ b/website/blog/authors.yml @@ -214,6 +214,14 @@ euan_johnston: - icon: fa-github url: https://github.com/euanjohnston-dev name: Euan Johnston +faith_mckenna: + image_url: /img/blog/authors/faith_pic.png + job_title: Senior Technical Instructor + links: + - icon: fa-linkedin + url: https://www.linkedin.com/in/faithlierheimer/ + name: Faith McKenna + organization: dbt Labs filip_byrén: image_url: /img/blog/authors/filip-eqt.png job_title: VP and Software Architect @@ -275,6 +283,14 @@ jeremy_cohen: job_title: Product Manager name: Jeremy Cohen organization: dbt Labs +jerrie_kumalah_kenney: + image_url: /img/blog/authors/jerrie.jpg + job_title: Resident Architect + links: + - icon: fa-linkedin + url: https://www.linkedin.com/in/jerriekumalah/ + name: Jerrie Kumalah Kenney + organization: dbt Labs jess_williams: image_url: /img/blog/authors/jess.png job_title: Head of Professional Services @@ -606,4 +622,4 @@ yu_ishikawa: - icon: fa-linkedin url: https://www.linkedin.com/in/yuishikawa0301 name: Yu Ishikawa - organization: Ubie \ No newline at end of file + organization: Ubie diff --git a/website/dbt-versions.js b/website/dbt-versions.js index 825af8ac6ee..3e59b926b80 100644 --- a/website/dbt-versions.js +++ b/website/dbt-versions.js @@ -16,11 +16,11 @@ exports.versions = [ { version: "1.10", - customDisplay: "Cloud (Versionless)", + customDisplay: "Cloud (Latest)", }, { version: "1.9", - isPrerelease: true, + EOLDate: "2025-12-08", }, { version: "1.8", diff --git a/website/docs/best-practices/how-we-structure/2-staging.md b/website/docs/best-practices/how-we-structure/2-staging.md index 8eb91ff5b7b..1f52a4a9a00 100644 --- a/website/docs/best-practices/how-we-structure/2-staging.md +++ b/website/docs/best-practices/how-we-structure/2-staging.md @@ -223,4 +223,4 @@ This is a welcome change for many of us who have become used to applying the sam :::info Development flow versus DAG order. This guide follows the order of the DAG, so we can get a holistic picture of how these three primary layers build on each other towards fueling impactful data products. It’s important to note though that developing models does not typically move linearly through the DAG. Most commonly, we should start by mocking out a design in a spreadsheet so we know we’re aligned with our stakeholders on output goals. Then, we’ll want to write the SQL to generate that output, and identify what tables are involved. Once we have our logic and dependencies, we’ll make sure we’ve staged all the necessary atomic pieces into the project, then bring them together based on the logic we wrote to generate our mart. Finally, with a functioning model flowing in dbt, we can start refactoring and optimizing that mart. By splitting the logic up and moving parts back upstream into intermediate models, we ensure all of our models are clean and readable, the story of our DAG is clear, and we have more surface area to apply thorough testing. -:::info +::: diff --git a/website/docs/best-practices/how-we-style/2-how-we-style-our-sql.md b/website/docs/best-practices/how-we-style/2-how-we-style-our-sql.md index 8c61e63b888..35e025faf3f 100644 --- a/website/docs/best-practices/how-we-style/2-how-we-style-our-sql.md +++ b/website/docs/best-practices/how-we-style/2-how-we-style-our-sql.md @@ -8,8 +8,8 @@ id: 2-how-we-style-our-sql - ☁️ Use [SQLFluff](https://sqlfluff.com/) to maintain these style rules automatically. - Customize `.sqlfluff` configuration files to your needs. - Refer to our [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for the rules we use in our own projects. - - - Exclude files and directories by using a standard `.sqlfluffignore` file. Learn more about the syntax in the [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2). + - Exclude files and directories by using a standard `.sqlfluffignore` file. Learn more about the syntax in the [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration/index.html). + - Excluding unnecessary folders and files (such as `target/`, `dbt_packages/`, and `macros/`) can speed up linting, improve run times, and help you avoid irrelevant logs. - 👻 Use Jinja comments (`{# #}`) for comments that should not be included in the compiled SQL. - ⏭️ Use trailing commas. - 4️⃣ Indents should be four spaces. diff --git a/website/docs/docs/build/conversion-metrics.md b/website/docs/docs/build/conversion-metrics.md index 2ef2c3910b9..2d227f4a703 100644 --- a/website/docs/docs/build/conversion-metrics.md +++ b/website/docs/docs/build/conversion-metrics.md @@ -20,28 +20,29 @@ The specification for conversion metrics is as follows: Note that we use the double colon (::) to indicate whether a parameter is nested within another parameter. So for example, `query_params::metrics` means the `metrics` parameter is nested under `query_params`. ::: -| Parameter | Description | Type | -| --- | --- | --- | -| `name` | The name of the metric. | Required | -| `description` | The description of the metric. | Optional | -| `type` | The type of metric (such as derived, ratio, and so on.). In this case, set as 'conversion' | Required | -| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | -| `type_params` | Specific configurations for each metric type. | Required | -| `conversion_type_params` | Additional configuration specific to conversion metrics. | Required | -| `entity` | The entity for each conversion event. | Required | -| `calculation` | Method of calculation. Either `conversion_rate` or `conversions`. Defaults to `conversion_rate`. | Optional | -| `base_measure` | A list of base measure inputs | Required | -| `base_measure:name` | The base conversion event measure. | Required | -| `base_measure:fill_nulls_with` | Set the value in your metric definition instead of null (such as zero). | Optional | -| `base_measure:join_to_timespine` | Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. | Optional | -| `conversion_measure` | A list of conversion measure inputs. | Required | -| `conversion_measure:name` | The base conversion event measure.| Required | -| `conversion_measure:fill_nulls_with` | Set the value in your metric definition instead of null (such as zero). | Optional | -| `conversion_measure:join_to_timespine` | Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. | Optional | -| `window` | The time window for the conversion event, such as 7 days, 1 week, 3 months. Defaults to infinity. | Optional | -| `constant_properties` | List of constant properties. | Optional | -| `base_property` | The property from the base semantic model that you want to hold constant. | Optional | -| `conversion_property` | The property from the conversion semantic model that you want to hold constant. | Optional | +| Parameter | Description | Required | Type | +| --- | --- | --- | --- | +| `name` | The name of the metric. | Required | String | +| `description` | The description of the metric. | Optional | String | +| `type` | The type of metric (such as derived, ratio, and so on.). In this case, set as 'conversion'. | Required | String | +| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | String | +| `type_params` | Specific configurations for each metric type. | Required | Dict | +| `conversion_type_params` | Additional configuration specific to conversion metrics. | Required | Dict | +| `entity` | The entity for each conversion event. | Required | String | +| `calculation` | Method of calculation. Either `conversion_rate` or `conversions`. Defaults to `conversion_rate`. | Optional | String | +| `base_measure` | A list of base measure inputs. | Required | Dict | +| `base_measure:name` | The base conversion event measure. | Required | String | +| `base_measure:fill_nulls_with` | Set the value in your metric definition instead of null (such as zero). | Optional | String | +| `base_measure:join_to_timespine` | Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. | Optional | Boolean | +| `base_measure:filter` | Optional `filter` used to apply to the base measure. | Optional | String | +| `conversion_measure` | A list of conversion measure inputs. | Required | Dict | +| `conversion_measure:name` | The base conversion event measure.| Required | String | +| `conversion_measure:fill_nulls_with` | Set the value in your metric definition instead of null (such as zero). | Optional | String | +| `conversion_measure:join_to_timespine` | Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. | Optional | Boolean | +| `window` | The time window for the conversion event, such as 7 days, 1 week, 3 months. Defaults to infinity. | Optional | String | +| `constant_properties` | List of constant properties. | Optional | List | +| `base_property` | The property from the base semantic model that you want to hold constant. | Optional | String | +| `conversion_property` | The property from the conversion semantic model that you want to hold constant. | Optional | String | Refer to [additional settings](#additional-settings) to learn how to customize conversion metrics with settings for null values, calculation type, and constant properties. @@ -61,6 +62,7 @@ metrics: name: The name of the measure # Required fill_nulls_with: Set the value in your metric definition instead of null (such as zero) # Optional join_to_timespine: true/false # Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. # Optional + filter: The filter used to apply to the base measure. # Optional conversion_measure: name: The name of the measure # Required fill_nulls_with: Set the value in your metric definition instead of null (such as zero) # Optional @@ -105,13 +107,14 @@ Next, define a conversion metric as follows: - name: visit_to_buy_conversion_rate_7d description: "Conversion rate from visiting to transaction in 7 days" type: conversion - label: Visit to Buy Conversion Rate (7-day window) + label: Visit to buy conversion rate (7-day window) type_params: conversion_type_params: base_measure: name: visits fill_nulls_with: 0 - conversion_measure: sellers + filter: {{ Dimension('visits__referrer_id') }} = 'facebook' + conversion_measure: name: sellers entity: user window: 7 days diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md index b44918d2fbd..24596be8b3d 100644 --- a/website/docs/docs/build/cumulative-metrics.md +++ b/website/docs/docs/build/cumulative-metrics.md @@ -18,21 +18,21 @@ Note that we use the double colon (::) to indicate whether a parameter is nested -| Parameter |
Description
| Type | -|-------------|---------------------------------------------------|-----------| -| `name` | The name of the metric. | Required | -| `description` | The description of the metric. | Optional | -| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required | -| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | -| `type_params` | The type parameters of the metric. Supports nested parameters indicated by the double colon, such as `type_params::measure`. | Required | -| `type_params::measure` | The measure associated with the metric. Supports both shorthand (string) and object syntax. The shorthand is used if only the name is needed, while the object syntax allows specifying additional attributes. | Required | -| `measure::name` | The name of the measure being referenced. Required if using object syntax for `type_params::measure`. | Optional | -| `measure::fill_nulls_with` | Sets a value (for example, 0) to replace nulls in the metric definition. | Optional | -| `measure::join_to_timespine` | Boolean indicating if the aggregated measure should be joined to the time spine table to fill in missing dates. Default is `false`. | Optional | -| `type_params::cumulative_type_params` | Configures the attributes like `window`, `period_agg`, and `grain_to_date` for cumulative metrics. | Optional | -| `cumulative_type_params::window` | Specifies the accumulation window, such as `1 month`, `7 days`, or `1 year`. Cannot be used with `grain_to_date`. | Optional | -| `cumulative_type_params::grain_to_date` | Sets the accumulation grain, such as `month`, restarting accumulation at the beginning of each specified grain period. Cannot be used with `window`. | Optional | -| `cumulative_type_params::period_agg` | Defines how to aggregate the cumulative metric when summarizing data to a different granularity: `first`, `last`, or `average`. Defaults to `first` if `window` is not specified. | Optional | +| Parameter |
Description
| Required | Type | +|-------------|---------------------------------------------------|----------|-----------| +| `name` | The name of the metric. | Required | String | +| `description` | The description of the metric. | Optional | String | +| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required | String | +| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | String | +| `type_params` | The type parameters of the metric. Supports nested parameters indicated by the double colon, such as `type_params::measure`. | Required | Dict | +| `type_params::measure` | The measure associated with the metric. Supports both shorthand (string) and object syntax. The shorthand is used if only the name is needed, while the object syntax allows specifying additional attributes. | Required | Dict | +| `measure::name` | The name of the measure being referenced. Required if using object syntax for `type_params::measure`. | Optional | String | +| `measure::fill_nulls_with` | Sets a value (for example, 0) to replace nulls in the metric definition. | Optional | Integer or string | +| `measure::join_to_timespine` | Boolean indicating if the aggregated measure should be joined to the time spine table to fill in missing dates. Default is `false`. | Optional | Boolean | +| `type_params::cumulative_type_params` | Configures the attributes like `window`, `period_agg`, and `grain_to_date` for cumulative metrics. | Optional | Dict | +| `cumulative_type_params::window` | Specifies the accumulation window, such as `1 month`, `7 days`, or `1 year`. Cannot be used with `grain_to_date`. | Optional | String | +| `cumulative_type_params::grain_to_date` | Sets the accumulation grain, such as `month`, restarting accumulation at the beginning of each specified grain period. Cannot be used with `window`. | Optional | String | +| `cumulative_type_params::period_agg` | Defines how to aggregate the cumulative metric when summarizing data to a different granularity: `first`, `last`, or `average`. Defaults to `first` if `window` is not specified. | Optional | String | diff --git a/website/docs/docs/build/custom-target-names.md b/website/docs/docs/build/custom-target-names.md index ac7036de572..218fec4283d 100644 --- a/website/docs/docs/build/custom-target-names.md +++ b/website/docs/docs/build/custom-target-names.md @@ -24,6 +24,6 @@ To set a custom target name for a job in dbt Cloud, configure the **Target Name* ## dbt Cloud IDE -When developing in dbt Cloud, you can set a custom target name in your development credentials. Go to your account (from the gear menu in the top right hand corner), select the project under **Credentials**, and update the target name. +When developing in dbt Cloud, you can set a custom target name in your development credentials. Click your account name above the profile icon in the left panel, select **Account settings**, then go to **Credentials**. Choose the project to update the target name. diff --git a/website/docs/docs/build/data-tests.md b/website/docs/docs/build/data-tests.md index afe4719768c..af48e0af267 100644 --- a/website/docs/docs/build/data-tests.md +++ b/website/docs/docs/build/data-tests.md @@ -66,7 +66,9 @@ having total_amount < 0 -The name of this test is the name of the file: `assert_total_payment_amount_is_positive`. +The name of this test is the name of the file: `assert_total_payment_amount_is_positive`. + +Note, you won't need to include semicolons (;) at the end of the SQL statement in your singular test files as it can cause your test to fail. To add a description to a singular test in your project, add a `.yml` file to your `tests` directory, for example, `tests/schema.yml` with the following content: diff --git a/website/docs/docs/build/derived-metrics.md b/website/docs/docs/build/derived-metrics.md index d5f2221907e..b6184aaeebf 100644 --- a/website/docs/docs/build/derived-metrics.md +++ b/website/docs/docs/build/derived-metrics.md @@ -10,18 +10,18 @@ In MetricFlow, derived metrics are metrics created by defining an expression usi The parameters, description, and type for derived metrics are: -| Parameter | Description | Type | -| --------- | ----------- | ---- | -| `name` | The name of the metric. | Required | -| `description` | The description of the metric. | Optional | -| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required | -| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | -| `type_params` | The type parameters of the metric. | Required | -| `expr` | The derived expression. You see validation warnings when the derived metric is missing an `expr` or the `expr` does not use all the input metrics. | Required | -| `metrics` | The list of metrics used in the derived metrics. | Required | -| `alias` | Optional alias for the metric that you can use in the expr. | Optional | -| `filter` | Optional filter to apply to the metric. | Optional | -| `offset_window` | Set the period for the offset window, such as 1 month. This will return the value of the metric one month from the metric time. | Optional | +| Parameter | Description | Required | Type | +| --------- | ----------- | ---- | ---- | +| `name` | The name of the metric. | Required | String | +| `description` | The description of the metric. | Optional | String | +| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required | String | +| `label` | Defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | String | +| `type_params` | The type parameters of the metric. | Required | Dict | +| `expr` | The derived expression. You'll see validation warnings when the derived metric is missing an `expr` or the `expr` does not use all the input metrics. | Required | String | +| `metrics` | The list of metrics used in the derived metrics. Each entry can include optional fields like `alias`, `filter`, or `offset_window`. | Required | List | +| `alias` | Optional alias for the metric that you can use in the `expr`. | Optional | String | +| `filter` | Optional filter to apply to the metric. | Optional | String | +| `offset_window` | Set the period for the offset window, such as 1 month. This will return the value of the metric one month from the metric time. | Optional | String | The following displays the complete specification for derived metrics, along with an example. diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md index 5026f4c45cd..975ae4d3160 100644 --- a/website/docs/docs/build/dimensions.md +++ b/website/docs/docs/build/dimensions.md @@ -14,14 +14,14 @@ Groups are defined within semantic models, alongside entities and measures, and All dimensions require a `name`, `type`, and can optionally include an `expr` parameter. The `name` for your Dimension must be unique within the same semantic model. -| Parameter | Description | Type | -| --------- | ----------- | ---- | -| `name` | Refers to the name of the group that will be visible to the user in downstream tools. It can also serve as an alias if the column name or SQL query reference is different and provided in the `expr` parameter.

Dimension names should be unique within a semantic model, but they can be non-unique across different models as MetricFlow uses [joins](/docs/build/join-logic) to identify the right dimension. | Required | -| `type` | Specifies the type of group created in the semantic model. There are two types:

- **Categorical**: Describe attributes or features like geography or sales region.
- **Time**: Time-based dimensions like timestamps or dates. | Required | -| `type_params` | Specific type params such as if the time is primary or used as a partition | Required | -| `description` | A clear description of the dimension | Optional | -| `expr` | Defines the underlying column or SQL query for a dimension. If no `expr` is specified, MetricFlow will use the column with the same name as the group. You can use the column name itself to input a SQL expression. | Optional | -| `label` | A recommended string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Optional | +| Parameter | Description | Required | Type | +| --------- | ----------- | ---- | ---- | +| `name` | Refers to the name of the group that will be visible to the user in downstream tools. It can also serve as an alias if the column name or SQL query reference is different and provided in the `expr` parameter.

Dimension names should be unique within a semantic model, but they can be non-unique across different models as MetricFlow uses [joins](/docs/build/join-logic) to identify the right dimension. | Required | String | +| `type` | Specifies the type of group created in the semantic model. There are two types:

- **Categorical**: Describe attributes or features like geography or sales region.
- **Time**: Time-based dimensions like timestamps or dates. | Required | String | +| `type_params` | Specific type params such as if the time is primary or used as a partition. | Required | Dict | +| `description` | A clear description of the dimension. | Optional | String | +| `expr` | Defines the underlying column or SQL query for a dimension. If no `expr` is specified, MetricFlow will use the column with the same name as the group. You can use the column name itself to input a SQL expression. | Optional | String | +| `label` | Defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Optional | String | Refer to the following for the complete specification for dimensions: diff --git a/website/docs/docs/build/environment-variables.md b/website/docs/docs/build/environment-variables.md index b87786ac596..99129cea8c9 100644 --- a/website/docs/docs/build/environment-variables.md +++ b/website/docs/docs/build/environment-variables.md @@ -32,7 +32,7 @@ There are four levels of environment variables: To set environment variables at the project and environment level, click **Deploy** in the top left, then select **Environments**. Click **Environments Variables** to add and update your environment variables. - + @@ -62,7 +62,10 @@ Every job runs in a specific, deployment environment, and by default, a job will **Overriding environment variables at the personal level** -You can also set a personal value override for an environment variable when you develop in the dbt-integrated developer environment (IDE). By default, dbt Cloud uses environment variable values set in the project's development environment. To see and override these values, click the gear icon in the top right. Under "Your Profile," click **Credentials** and select your project. Click **Edit** and make any changes in "Environment Variables." +You can also set a personal value override for an environment variable when you develop in the dbt-integrated developer environment (IDE). By default, dbt Cloud uses environment variable values set in the project's development environment. To see and override these values, from dbt Cloud: +- Click on your account name in the left side menu and select **Account settings**. +- Under the **Your profile** section, click **Credentials** and then select your project. +- Scroll to the **Environment variables** section and click **Edit** to make the necessary changes. @@ -115,7 +118,7 @@ The following environment variables are set automatically: - `DBT_ENV` — This key is reserved for the dbt Cloud application and will always resolve to 'prod'. For deployment runs only. - `DBT_CLOUD_ENVIRONMENT_NAME` — The name of the dbt Cloud environment in which `dbt` is running. -- `DBT_CLOUD_ENVIRONMENT_TYPE` — The type of dbt Cloud environment in which `dbt` is running. The valid values are `development` or `deployment`. +- `DBT_CLOUD_ENVIRONMENT_TYPE` — The type of dbt Cloud environment in which `dbt` is running. The valid values are `dev`, `staging`, or `prod`. It can be unset, so use a default like `{{env_var('DBT_CLOUD_ENVIRONMENT_TYPE', '')}}`. #### Run details diff --git a/website/docs/docs/build/exposures.md b/website/docs/docs/build/exposures.md index 1a85d5fb415..16dfd0e5f73 100644 --- a/website/docs/docs/build/exposures.md +++ b/website/docs/docs/build/exposures.md @@ -69,7 +69,7 @@ dbt test -s +exposure:weekly_jaffle_report ``` -When we generate the dbt Explorer site, you'll see the exposure appear: +When we generate the [dbt Explorer site](/docs/collaborate/explore-projects), you'll see the exposure appear: diff --git a/website/docs/docs/build/hooks-operations.md b/website/docs/docs/build/hooks-operations.md index 6cec2a673c0..842d3fb99a3 100644 --- a/website/docs/docs/build/hooks-operations.md +++ b/website/docs/docs/build/hooks-operations.md @@ -40,8 +40,6 @@ Hooks are snippets of SQL that are executed at different times: Hooks are a more-advanced capability that enable you to run custom SQL, and leverage database-specific actions, beyond what dbt makes available out-of-the-box with standard materializations and configurations. - - If (and only if) you can't leverage the [`grants` resource-config](/reference/resource-configs/grants), you can use `post-hook` to perform more advanced workflows: * Need to apply `grants` in a more complex way, which the dbt Core `grants` config doesn't (yet) support. diff --git a/website/docs/docs/build/incremental-microbatch.md b/website/docs/docs/build/incremental-microbatch.md index e1c39e6ae47..901f59a167c 100644 --- a/website/docs/docs/build/incremental-microbatch.md +++ b/website/docs/docs/build/incremental-microbatch.md @@ -8,7 +8,9 @@ id: "incremental-microbatch" :::info Microbatch -The `microbatch` strategy is available in beta for [dbt Cloud Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) and dbt Core v1.9. We have been developing it behind a flag to prevent unintended interactions with existing custom incremental strategies. To enable this feature, [set the environment variable](/docs/build/environment-variables#setting-and-overriding-environment-variables) `DBT_EXPERIMENTAL_MICROBATCH` to `True` in your dbt Cloud environments or wherever you're running dbt Core. +The new `microbatch` strategy is available in beta for [dbt Cloud "Latest"](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9. + +If you use a custom microbatch macro, set a [distinct behavior flag](/reference/global-configs/behavior-changes#custom-microbatch-strategy) in your `dbt_project.yml` to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the [microbatch strategy](#how-microbatch-compares-to-other-incremental-strategies). Read and participate in the discussion: [dbt-core#10672](https://github.com/dbt-labs/dbt-core/discussions/10672) @@ -20,17 +22,35 @@ Refer to [Supported incremental strategies by adapter](/docs/build/incremental-s Incremental models in dbt are a [materialization](/docs/build/materializations) designed to efficiently update your data warehouse tables by only transforming and loading _new or changed data_ since the last run. Instead of reprocessing an entire dataset every time, incremental models process a smaller number of rows, and then append, update, or replace those rows in the existing table. This can significantly reduce the time and resources required for your data transformations. -Microbatch incremental models make it possible to process transformations on very large time-series datasets with efficiency and resiliency. When dbt runs a microbatch model — whether for the first time, during incremental runs, or in specified backfills — it will split the processing into multiple queries (or "batches"), based on the `event_time` and `batch_size` you configure. +Microbatch is an incremental strategy designed for large time-series datasets: +- It relies solely on a time column ([`event_time`](/reference/resource-configs/event-time)) to define time-based ranges for filtering. Set the `event_time` column for your microbatch model and its direct parents (upstream models). Note, this is different to `partition_by`, which groups rows into partitions. +- It complements, rather than replaces, existing incremental strategies by focusing on efficiency and simplicity in batch processing. +- Unlike traditional incremental strategies, microbatch enables you to [reprocess failed batches](/docs/build/incremental-microbatch#retry), auto-detect [parallel batch execution](#parallel-batch-execution), and eliminate the need to implement complex conditional logic for [backfilling](#backfills). + +- Note, microbatch might not be the best strategy for all use cases. Consider other strategies for use cases such as not having a reliable `event_time` column or if you want more control over the incremental logic. Read more in [How `microbatch` compares to other incremental strategies](#how-microbatch-compares-to-other-incremental-strategies). + +### How microbatch works + +When dbt runs a microbatch model — whether for the first time, during incremental runs, or in specified backfills — it will split the processing into multiple queries (or "batches"), based on the `event_time` and `batch_size` you configure. + +Each "batch" corresponds to a single bounded time period (by default, a single day of data). Where other incremental strategies operate only on "old" and "new" data, microbatch models treat every batch as an atomic unit that can be built or replaced on its own. Each batch is independent and . + +This is a powerful abstraction that makes it possible for dbt to run batches [separately](#backfills), concurrently, and [retry](#retry) them independently. -Each "batch" corresponds to a single bounded time period (by default, a single day of data). Where other incremental strategies operate only on "old" and "new" data, microbatch models treat every batch as an atomic unit that can be built or replaced on its own. Each batch is independent and . This is a powerful abstraction that makes it possible for dbt to run batches separately — in the future, concurrently — and to retry them independently. +## Example -### Example +A `sessions` model aggregates and enriches data that comes from two other models: +- `page_views` is a large, time-series table. It contains many rows, new records almost always arrive after existing ones, and existing records rarely update. It uses the `page_view_start` column as its `event_time`. +- `customers` is a relatively small dimensional table. Customer attributes update often, and not in a time-based manner — that is, older customers are just as likely to change column values as newer customers. The customers model doesn't configure an `event_time` column. -A `sessions` model aggregates and enriches data that comes from two other models. -- `page_views` is a large, time-series table. It contains many rows, new records almost always arrive after existing ones, and existing records rarely update. -- `customers` is a relatively small dimensional table. Customer attributes update often, and not in a time-based manner — that is, older customers are just as likely to change column values as newer customers. +As a result: -The `page_view_start` column in `page_views` is configured as that model's `event_time`. The `customers` model does not configure an `event_time`. Therefore, each batch of `sessions` will filter `page_views` to the equivalent time-bounded batch, and it will not filter `customers` (a full scan for every batch). +- Each batch of `sessions` will filter `page_views` to the equivalent time-bounded batch. +- The `customers` table isn't filtered, resulting in a full scan for every batch. + +:::tip +In addition to configuring `event_time` for the target table, you should also specify it for any upstream models that you want to filter, even if they have different time columns. +::: @@ -42,13 +62,13 @@ models: ``` -We run the `sessions` model on October 1, 2024, and then again on October 2. It produces the following queries: +We run the `sessions` model for October 1, 2024, and then again for October 2. It produces the following queries: -The `event_time` for the `sessions` model is set to `session_start`, which marks the beginning of a user’s session on the website. This setting allows dbt to combine multiple page views (each tracked by their own `page_view_start` timestamps) into a single session. This way, `session_start` differentiates the timing of individual page views from the broader timeframe of the entire user session. +The [`event_time`](/reference/resource-configs/event-time) for the `sessions` model is set to `session_start`, which marks the beginning of a user’s session on the website. This setting allows dbt to combine multiple page views (each tracked by their own `page_view_start` timestamps) into a single session. This way, `session_start` differentiates the timing of individual page views from the broader timeframe of the entire user session. @@ -156,22 +176,65 @@ It does not matter whether the table already contains data for that day. Given t -### Relevant configs +## Relevant configs Several configurations are relevant to microbatch models, and some are required: -| Config | Type | Description | Default | -|----------|------|---------------|---------| -| `event_time` | Column (required) | The column indicating "at what time did the row occur." Required for your microbatch model and any direct parents that should be filtered. | N/A | -| `begin` | Date (required) | The "beginning of time" for the microbatch model. This is the starting point for any initial or full-refresh builds. For example, a daily-grain microbatch model run on `2024-10-01` with `begin = '2023-10-01` will process 366 batches (it's a leap year!) plus the batch for "today." | N/A | -| `batch_size` | String (required) | The granularity of your batches. Supported values are `hour`, `day`, `month`, and `year` | N/A | -| `lookback` | Integer (optional) | Process X batches prior to the latest bookmark to capture late-arriving records. | `1` | + +| Config | Description | Default | Type | Required | +|----------|---------------|---------|------|---------| +| [`event_time`](/reference/resource-configs/event-time) | The column indicating "at what time did the row occur." Required for your microbatch model and any direct parents that should be filtered. | N/A | Column | Required | +| [`begin`](/reference/resource-configs/begin) | The "beginning of time" for the microbatch model. This is the starting point for any initial or full-refresh builds. For example, a daily-grain microbatch model run on `2024-10-01` with `begin = '2023-10-01` will process 366 batches (it's a leap year!) plus the batch for "today." | N/A | Date | Required | +| [`batch_size`](/reference/resource-configs/batch-size) | The granularity of your batches. Supported values are `hour`, `day`, `month`, and `year` | N/A | String | Required | +| [`lookback`](/reference/resource-configs/lookback) | Process X batches prior to the latest bookmark to capture late-arriving records. | `1` | Integer | Optional | +| [`concurrent_batches`](/reference/resource-properties/concurrent_batches) | An override for whether batches run concurrently (at the same time) or sequentially (one after the other). | `None` | Boolean | Optional | +### Required configs for specific adapters +Some adapters require additional configurations for the microbatch strategy. This is because each adapter implements the microbatch strategy differently. + +The following table lists the required configurations for the specific adapters, in addition to the standard microbatch configs: + +| Adapter | `unique_key` config | `partition_by` config | +|----------|------------------|--------------------| +| [`dbt-postgres`](/reference/resource-configs/postgres-configs#incremental-materialization-strategies) | ✅ Required | N/A | +| [`dbt-spark`](/reference/resource-configs/spark-configs#incremental-models) | N/A | ✅ Required | +| [`dbt-bigquery`](/reference/resource-configs/bigquery-configs#merge-behavior-incremental-models) | N/A | ✅ Required | + +For example, if you're using `dbt-postgres`, configure `unique_key` as follows: + + + +```sql +{{ config( + materialized='incremental', + incremental_strategy='microbatch', + unique_key='sales_id', ## required for dbt-postgres + event_time='transaction_date', + begin='2023-01-01', + batch_size='day' +) }} + +select + sales_id, + transaction_date, + customer_id, + product_id, + total_amount +from {{ source('sales', 'transactions') }} + +``` + + In this example, `unique_key` is required because `dbt-postgres` microbatch uses the `merge` strategy, which needs a `unique_key` to identify which rows in the data warehouse need to get merged. Without a `unique_key`, dbt won't be able to match rows between the incoming batch and the existing table. + + + +### Full refresh + As a best practice, we recommend configuring `full_refresh: False` on microbatch models so that they ignore invocations with the `--full-refresh` flag. If you need to reprocess historical data, do so with a targeted backfill that specifies explicit start and end dates. -### Usage +## Usage **You must write your model query to process (read and return) exactly one "batch" of data**. This is a simplifying assumption and a powerful one: - You don’t need to think about `is_incremental` filtering @@ -188,7 +251,7 @@ During standard incremental runs, dbt will process batches according to the curr **Note:** If there’s an upstream model that configures `event_time`, but you *don’t* want the reference to it to be filtered, you can specify `ref('upstream_model').render()` to opt-out of auto-filtering. This isn't generally recommended — most models that configure `event_time` are fairly large, and if the reference is not filtered, each batch will perform a full scan of this input table. -### Backfills +## Backfills Whether to fix erroneous source data or retroactively apply a change in business logic, you may need to reprocess a large amount of historical data. @@ -203,13 +266,13 @@ dbt run --event-time-start "2024-09-01" --event-time-end "2024-09-04" -### Retry +## Retry If one or more of your batches fail, you can use `dbt retry` to reprocess _only_ the failed batches. ![Partial retry](https://github.com/user-attachments/assets/f94c4797-dcc7-4875-9623-639f70c97b8f) -### Timezones +## Timezones For now, dbt assumes that all values supplied are in UTC: @@ -220,7 +283,127 @@ For now, dbt assumes that all values supplied are in UTC: While we may consider adding support for custom time zones in the future, we also believe that defining these values in UTC makes everyone's lives easier. -## How `microbatch` compares to other incremental strategies? +## Parallel batch execution + +The microbatch strategy offers the benefit of updating a model in smaller, more manageable batches. Depending on your use case, configuring your microbatch models to run in parallel offers faster processing, in comparison to running batches sequentially. + +Parallel batch execution means that multiple batches are processed at the same time, instead of one after the other (sequentially) for faster processing of your microbatch models. + +dbt automatically detects whether a batch can be run in parallel in most cases, which means you don’t need to configure this setting. However, the [`concurrent_batches` config](/reference/resource-properties/concurrent_batches) is available as an override (not a gate), allowing you to specify whether batches should or shouldn’t be run in parallel in specific cases. + +For example, if you have a microbatch model with 12 batches, you can execute those batches to run in parallel. Specifically they'll run in parallel limited by the number of [available threads](/docs/running-a-dbt-project/using-threads). + +### Prerequisites + +To enable parallel execution, you must: + +- Use a supported adapter: + - Snowflake + - Databricks + - More adapters coming soon! + - We'll be continuing to test and add concurrency support for adapters. This means that some adapters might get concurrency support _after_ the 1.9 initial release. + +- Meet [additional conditions](#how-parallel-batch-execution-works) described in the following section. + +### How parallel batch execution works + +A batch can only run in parallel if all of these conditions are met: + +| Condition | Parallel execution | Sequential execution| +| ---------------| :------------------: | :----------: | +| **Not** the first batch | ✅ | - | +| **Not** the last batch | ✅ | - | +| [Adapter supports](#prerequisites) parallel batches | ✅ | - | + + +After checking for the conditions in the previous table — and if `concurrent_batches` value isn't set, dbt will intelligently auto-detect if the model invokes the [`{{ this }}`](/reference/dbt-jinja-functions/this) Jinja function. If it references `{{ this }}`, the batches will run sequentially since `{{ this }}` represents the database of the current model and referencing the same relation causes conflict. + +Otherwise, if `{{ this }}` isn't detected (and other conditions are met), the batches will run in parallel, which can be overriden when you [set a value for `concurrent_batches`](/reference/resource-properties/concurrent_batches). + +### Parallel or sequential execution + +Choosing between parallel batch execution and sequential processing depends on the specific requirements of your use case. + +- Parallel batch execution is faster but requires logic independent of batch execution order. For example, if you're developing a data pipeline for a system that processes user transactions in batches, each batch is executed in parallel for better performance. However, the logic used to process each transaction shouldn't depend on the order of how batches are executed or completed. +- Sequential processing is slower but essential for calculations like [cumulative metrics](/docs/build/cumulative) in microbatch models. It processes data in the correct order, allowing each step to build on the previous one. + + + +### Configure `concurrent_batches` + +By default, dbt auto-detects whether batches can run in parallel for microbatch models, and this works correctly in most cases. However, you can override dbt's detection by setting the [`concurrent_batches` config](/reference/resource-properties/concurrent_batches) in your `dbt_project.yml` or model `.sql` file to specify parallel or sequential execution, given you meet all the [conditions](#prerequisites): + + + + + + +```yaml +models: + +concurrent_batches: true # value set to true to run batches in parallel +``` + + + + + + + + +```sql +{{ + config( + materialized='incremental', + incremental_strategy='microbatch', + event_time='session_start', + begin='2020-01-01', + batch_size='day + concurrent_batches=true, # value set to true to run batches in parallel + ... + ) +}} + +select ... +``` + + + + +## How microbatch compares to other incremental strategies + +As data warehouses roll out new operations for concurrently replacing/upserting data partitions, we may find that the new operation for the data warehouse is more efficient than what the adapter uses for microbatch. In such instances, we reserve the right the update the default operation for microbatch, so long as it works as intended/documented for models that fit the microbatch paradigm. Most incremental models rely on the end user (you) to explicitly tell dbt what "new" means, in the context of each model, by writing a filter in an `{% if is_incremental() %}` conditional block. You are responsible for crafting this SQL in a way that queries [`{{ this }}`](/reference/dbt-jinja-functions/this) to check when the most recent record was last loaded, with an optional look-back window for late-arriving records. diff --git a/website/docs/docs/build/incremental-models.md b/website/docs/docs/build/incremental-models.md index 2968496290a..0560797c9bc 100644 --- a/website/docs/docs/build/incremental-models.md +++ b/website/docs/docs/build/incremental-models.md @@ -114,7 +114,7 @@ When you define a `unique_key`, you'll see this behavior for each row of "new" d Please note that if there's a unique_key with more than one row in either the existing target table or the new incremental rows, the incremental model may fail depending on your database and [incremental strategy](/docs/build/incremental-strategy). If you're having issues running an incremental model, it's a good idea to double check that the unique key is truly unique in both your existing database table and your new incremental rows. You can [learn more about surrogate keys here](https://www.getdbt.com/blog/guide-to-surrogate-key). :::info -While common incremental strategies, such as`delete+insert` + `merge`, might use `unique_key`, others don't. For example, the `insert_overwrite` strategy does not use `unique_key`, because it operates on partitions of data rather than individual rows. For more information, see [About incremental_strategy](/docs/build/incremental-strategy). +While common incremental strategies, such as `delete+insert` + `merge`, might use `unique_key`, others don't. For example, the `insert_overwrite` strategy does not use `unique_key`, because it operates on partitions of data rather than individual rows. For more information, see [About incremental_strategy](/docs/build/incremental-strategy). ::: #### `unique_key` example @@ -156,15 +156,17 @@ Building this model incrementally without the `unique_key` parameter would resul ## How do I rebuild an incremental model? If your incremental model logic has changed, the transformations on your new rows of data may diverge from the historical transformations, which are stored in your target table. In this case, you should rebuild your incremental model. -To force dbt to rebuild the entire incremental model from scratch, use the `--full-refresh` flag on the command line. This flag will cause dbt to drop the existing target table in the database before rebuilding it for all-time. +To force dbt to rebuild the entire incremental model from scratch, use the `--full-refresh` flag on the command line. This flag will cause dbt to drop the existing target table in the database before rebuilding it for all-time. ```bash $ dbt run --full-refresh --select my_incremental_model+ ``` + It's also advisable to rebuild any downstream models, as indicated by the trailing `+`. -For detailed usage instructions, check out the [dbt run](/reference/commands/run) documentation. +You can optionally use the [`full_refresh config`](/reference/resource-configs/full_refresh) to set a resource to always or never full-refresh at the project or resource level. If specified as true or false, the `full_refresh` config will take precedence over the presence or absence of the `--full-refresh` flag. +For detailed usage instructions, check out the [dbt run](/reference/commands/run) documentation. ## What if the columns of my incremental model change? @@ -212,11 +214,11 @@ Currently, `on_schema_change` only tracks top-level column changes. It does not ### Default behavior -This is the behavior if `on_schema_change: ignore`, which is set by default, and on older versions of dbt. +This is the behavior of `on_schema_change: ignore`, which is set by default. If you add a column to your incremental model, and execute a `dbt run`, this column will _not_ appear in your target table. -Similarly, if you remove a column from your incremental model, and execute a `dbt run`, this column will _not_ be removed from your target table. +If you remove a column from your incremental model and execute a `dbt run`, `dbt run` will fail. Instead, whenever the logic of your incremental changes, execute a full-refresh run of both your incremental model and any downstream models. diff --git a/website/docs/docs/build/incremental-strategy.md b/website/docs/docs/build/incremental-strategy.md index 30de135b09b..9176e962a3a 100644 --- a/website/docs/docs/build/incremental-strategy.md +++ b/website/docs/docs/build/incremental-strategy.md @@ -27,13 +27,13 @@ Click the name of the adapter in the below table for more information about supp | Data platform adapter | `append` | `merge` | `delete+insert` | `insert_overwrite` | `microbatch` | |-----------------------|:--------:|:-------:|:---------------:|:------------------:|:-------------------:| | [dbt-postgres](/reference/resource-configs/postgres-configs#incremental-materialization-strategies) | ✅ | ✅ | ✅ | | ✅ | -| [dbt-redshift](/reference/resource-configs/redshift-configs#incremental-materialization-strategies) | ✅ | ✅ | ✅ | | | +| [dbt-redshift](/reference/resource-configs/redshift-configs#incremental-materialization-strategies) | ✅ | ✅ | ✅ | | ✅ | | [dbt-bigquery](/reference/resource-configs/bigquery-configs#merge-behavior-incremental-models) | | ✅ | | ✅ | ✅ | | [dbt-spark](/reference/resource-configs/spark-configs#incremental-models) | ✅ | ✅ | | ✅ | ✅ | -| [dbt-databricks](/reference/resource-configs/databricks-configs#incremental-models) | ✅ | ✅ | | ✅ | | +| [dbt-databricks](/reference/resource-configs/databricks-configs#incremental-models) | ✅ | ✅ | | ✅ | ✅ | | [dbt-snowflake](/reference/resource-configs/snowflake-configs#merge-behavior-incremental-models) | ✅ | ✅ | ✅ | | ✅ | | [dbt-trino](/reference/resource-configs/trino-configs#incremental) | ✅ | ✅ | ✅ | | | -| [dbt-fabric](/reference/resource-configs/fabric-configs#incremental) | ✅ | ✅ | ✅ | | | +| [dbt-fabric](/reference/resource-configs/fabric-configs#incremental) | ✅ | | ✅ | | | | [dbt-athena](/reference/resource-configs/athena-configs#incremental-models) | ✅ | ✅ | | ✅ | | ### Configuring incremental strategy @@ -241,7 +241,13 @@ select * from {{ ref("some_model") }} ### Custom strategies -Starting from dbt version 1.2 and onwards, users have an easier alternative to [creating an entirely new materialization](/guides/create-new-materializations). They define and use their own "custom" incremental strategies by: +:::note limited support + +Custom strategies are not currently supported on the BigQuery and Spark adapters. + +::: + +From dbt v1.2 and onwards, users have an easier alternative to [creating an entirely new materialization](/guides/create-new-materializations). They define and use their own "custom" incremental strategies by: 1. Defining a macro named `get_incremental_STRATEGY_sql`. Note that `STRATEGY` is a placeholder and you should replace it with the name of your custom incremental strategy. 2. Configuring `incremental_strategy: STRATEGY` within an incremental model. @@ -289,6 +295,8 @@ For example, a user-defined strategy named `insert_only` can be defined and used +If you use a custom microbatch macro, set a [`require_batched_execution_for_custom_microbatch_strategy` behavior flag](/reference/global-configs/behavior-changes#custom-microbatch-strategy) in your `dbt_project.yml` to enable batched execution of your custom strategy. + ### Custom strategies from a package To use the `merge_null_safe` custom incremental strategy from the `example` package: diff --git a/website/docs/docs/build/materializations.md b/website/docs/docs/build/materializations.md index 5deb1e7ce92..723acf87414 100644 --- a/website/docs/docs/build/materializations.md +++ b/website/docs/docs/build/materializations.md @@ -111,7 +111,7 @@ When using the `table` materialization, your model is rebuilt as a - + It's common in analytics engineering to have a date dimension or "time spine" table as a base table for different types of time-based joins and aggregations. The structure of this table is typically a base column of daily or hourly dates, with additional columns for other time grains, like fiscal quarters, defined based on the base column. You can join other tables to the time spine on the base column to calculate metrics like revenue at a point in time, or to aggregate to a specific time grain. @@ -108,7 +108,7 @@ models: - It needs to reference a column defined under the `columns` key, in this case, `date_hour` and `date_day`, respectively. - It sets the granularity at the column-level using the `granularity` key, in this case, `hour` and `day`, respectively. - MetricFlow will use the `standard_granularity_column` as the join key when joining the time spine table to another source table. -- [The `custom_granularities` field](#custom-calendar), (available in Versionless and dbt v1.9 and higher) lets you specify non-standard time periods like `fiscal_year` or `retail_month` that your organization may use. +- [The `custom_granularities` field](#custom-calendar), (available in dbt Cloud Latest and dbt Core v1.9 and higher) lets you specify non-standard time periods like `fiscal_year` or `retail_month` that your organization may use. For an example project, refer to our [Jaffle shop](https://github.com/dbt-labs/jaffle-sl-template/blob/main/models/marts/_models.yml) example. @@ -179,8 +179,8 @@ final as ( select * from final -- filter the time spine to a specific range -where date_day > dateadd(year, -4, current_timestamp()) -and date_day < dateadd(day, 30, current_timestamp()) +where date_day > date_add(DATE(current_timestamp()), INTERVAL -4 YEAR) +and date_day < date_add(DATE(current_timestamp()), INTERVAL 30 DAY) ``` @@ -310,9 +310,7 @@ You only need to include the `date_day` column in the table. MetricFlow can hand -The ability to configure custom calendars, such as a fiscal calendar, is available in [dbt Cloud Versionless](/docs/dbt-versions/versionless-cloud) or dbt Core [v1.9 and higher](/docs/dbt-versions/core). - -To access this feature, [upgrade to Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) or your dbt Core version to v1.9 or higher. +The ability to configure custom calendars, such as a fiscal calendar, is available now in [the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks), and it will be available in [dbt Core v1.9+](/docs/dbt-versions/core-upgrade/upgrading-to-v1.9). diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md index 7021a6d7330..57cdd929acb 100644 --- a/website/docs/docs/build/metrics-overview.md +++ b/website/docs/docs/build/metrics-overview.md @@ -15,15 +15,15 @@ This article explains the different supported metric types you can add to your d -| Parameter | Description | Type | -| --------- | ----------- | ---- | -| `name` | Provide the reference name for the metric. This name must be a unique metric name and can consist of lowercase letters, numbers, and underscores. | Required | -| `description` | Describe your metric. | Optional | -| `type` | Define the type of metric, which can be `conversion`, `cumulative`, `derived`, `ratio`, or `simple`. | Required | -| `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required | -| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | -| `config` | Use the [`config`](/reference/resource-properties/config) property to specify configurations for your metric. Supports [`meta`](/reference/resource-configs/meta), [`group`](/reference/resource-configs/group), and [`enabled`](/reference/resource-configs/enabled) configurations. | Optional | -| `filter` | You can optionally add a [filter](#filters) string to any metric type, applying filters to dimensions, entities, time dimensions, or other metrics during metric computation. Consider it as your WHERE clause. | Optional | +| Parameter | Description | Required | Type | +| --------- | ----------- | ---- | ---- | +| `name` | Provide the reference name for the metric. This name must be a unique metric name and can consist of lowercase letters, numbers, and underscores. | Required | String | +| `description` | Describe your metric. | Optional | String | +| `type` | Define the type of metric, which can be `conversion`, `cumulative`, `derived`, `ratio`, or `simple`. | Required | String | +| `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required | Dict | +| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | String | +| `config` | Use the [`config`](/reference/resource-properties/config) property to specify configurations for your metric. Supports [`meta`](/reference/resource-configs/meta), [`group`](/reference/resource-configs/group), and [`enabled`](/reference/resource-configs/enabled) configurations. | Optional | Dict | +| `filter` | You can optionally add a [filter](#filters) string to any metric type, applying filters to dimensions, entities, time dimensions, or other metrics during metric computation. Consider it as your WHERE clause. | Optional | String | Here's a complete example of the metrics spec configuration: @@ -52,16 +52,16 @@ metrics: -| Parameter | Description | Type | -| --------- | ----------- | ---- | -| `name` | Provide the reference name for the metric. This name must be unique amongst all metrics. | Required | -| `description` | Describe your metric. | Optional | -| `type` | Define the type of metric, which can be `simple`, `ratio`, `cumulative`, or `derived`. | Required | -| `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required | -| `config` | Provide the specific configurations for your metric. | Optional | -| `meta` | Use the [`meta` config](/reference/resource-configs/meta) to set metadata for a resource. | Optional | -| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | -| `filter` | You can optionally add a filter string to any metric type, applying filters to dimensions, entities, or time dimensions during metric computation. Consider it as your WHERE clause. | Optional | +| Parameter | Description | Required | Type | +| --------- | ----------- | ---- | ---- | +| `name` | Provide the reference name for the metric. This name must be unique amongst all metrics. | Required | String | +| `description` | Describe your metric. | Optional | String | +| `type` | Define the type of metric, which can be `simple`, `ratio`, `cumulative`, or `derived`. | Required | String | +| `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required | Dict | +| `config` | Provide the specific configurations for your metric. | Optional | Dict | +| `meta` | Use the [`meta` config](/reference/resource-configs/meta) to set metadata for a resource. | Optional | String | +| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | String | +| `filter` | You can optionally add a filter string to any metric type, applying filters to dimensions, entities, or time dimensions during metric computation. Consider it as your WHERE clause. | Optional | String | Here's a complete example of the metrics spec configuration: @@ -95,7 +95,8 @@ import SLCourses from '/snippets/_sl-course.md'; Default time granularity for metrics is useful if your time dimension has a very fine grain, like second or hour, but you typically query metrics rolled up at a coarser grain. -To set the default time granularity for metrics, you need to be on dbt Cloud Versionless or dbt v1.9 and higher. +Default time granularity for metrics is available now in [the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks), and it will be available in [dbt Core v1.9+](/docs/dbt-versions/core-upgrade/upgrading-to-v1.9). + diff --git a/website/docs/docs/build/packages.md b/website/docs/docs/build/packages.md index 49cd7e00b1c..9ba4ceeaff5 100644 --- a/website/docs/docs/build/packages.md +++ b/website/docs/docs/build/packages.md @@ -162,7 +162,7 @@ Where `name: 'dbt_utils'` specifies the subfolder of `dbt_packages` that's creat #### SSH Key Method (Command Line only) If you're using the Command Line, private packages can be cloned via SSH and an SSH key. -When you use SSH keys to authenticate to your git remote server, you don’t need to supply your username and password each time. Read more about SSH keys, how to generate them, and how to add them to your git provider here: [Github](https://docs.github.com/en/github/authenticating-to-github/connecting-to-github-with-ssh) and [GitLab](https://docs.gitlab.com/ee/ssh/). +When you use SSH keys to authenticate to your git remote server, you don’t need to supply your username and password each time. Read more about SSH keys, how to generate them, and how to add them to your git provider here: [Github](https://docs.github.com/en/github/authenticating-to-github/connecting-to-github-with-ssh) and [GitLab](https://docs.gitlab.com/ee/user/ssh.html). diff --git a/website/docs/docs/build/python-models.md b/website/docs/docs/build/python-models.md index 811379a0d2c..eac477b03fd 100644 --- a/website/docs/docs/build/python-models.md +++ b/website/docs/docs/build/python-models.md @@ -598,6 +598,34 @@ Python models have capabilities that SQL models do not. They also have some draw - **These capabilities are very new.** As data warehouses develop new features, we expect them to offer cheaper, faster, and more intuitive mechanisms for deploying Python transformations. **We reserve the right to change the underlying implementation for executing Python models in future releases.** Our commitment to you is around the code in your model `.py` files, following the documented capabilities and guidance we're providing here. - **Lack of `print()` support.** The data platform runs and compiles your Python model without dbt's oversight. This means it doesn't display the output of commands such as Python's built-in [`print()`](https://docs.python.org/3/library/functions.html#print) function in dbt's logs. +- + + The following explains other methods you can use for debugging, such as writing messages to a dataframe column: + + - Using platform logs: Use your data platform's logs to debug your Python models. + - Return logs as a dataframe: Create a dataframe containing your logs and build it into the warehouse. + - Develop locally with DuckDB: Test and debug your models locally using DuckDB before deploying them. + + Here's an example of debugging in a Python model: + + ```python + def model(dbt, session): + dbt.config( + materialized = "table" + ) + + df = dbt.ref("my_source_table").df() + + # One option for debugging: write messages to temporary table column + # Pros: visibility + # Cons: won't work if table isn't building for some reason + msg = "something" + df["debugging"] = f"My debug message here: {msg}" + + return df + ``` + + As a general rule, if there's a transformation you could write equally well in SQL or Python, we believe that well-written SQL is preferable: it's more accessible to a greater number of colleagues, and it's easier to write code that's performant at scale. If there's a transformation you _can't_ write in SQL, or where ten lines of elegant and well-annotated Python could save you 1000 lines of hard-to-read Jinja-SQL, Python is the way to go. ## Specific data platforms {#specific-data-platforms} @@ -613,7 +641,8 @@ In their initial launch, Python models are supported on three of the most popula **Installing packages:** Snowpark supports several popular packages via Anaconda. Refer to the [complete list](https://repo.anaconda.com/pkgs/snowflake/) for more details. Packages are installed when your model is run. Different models can have different package dependencies. If you use third-party packages, Snowflake recommends using a dedicated virtual warehouse for best performance rather than one with many concurrent users. **Python version:** To specify a different python version, use the following configuration: -``` + +```python def model(dbt, session): dbt.config( materialized = "table", @@ -625,7 +654,7 @@ def model(dbt, session): **External access integrations and secrets**: To query external APIs within dbt Python models, use Snowflake’s [external access](https://docs.snowflake.com/en/developer-guide/external-network-access/external-network-access-overview) together with [secrets](https://docs.snowflake.com/en/developer-guide/external-network-access/secret-api-reference). Here are some additional configurations you can use: -``` +```python import pandas import snowflake.snowpark as snowpark @@ -645,20 +674,43 @@ def model(dbt, session: snowpark.Session): -**About "sprocs":** dbt submits Python models to run as _stored procedures_, which some people call _sprocs_ for short. By default, dbt will create a named sproc containing your model's compiled Python code, and then _call_ it to execute. Snowpark has an Open Preview feature for _temporary_ or _anonymous_ stored procedures ([docs](https://docs.snowflake.com/en/sql-reference/sql/call-with.html)), which are faster and leave a cleaner query history. You can switch this feature on for your models by configuring `use_anonymous_sproc: True`. We plan to switch this on for all dbt + Snowpark Python models starting with the release of dbt Core version 1.4. +**About "sprocs":** dbt submits Python models to run as _stored procedures_, which some people call _sprocs_ for short. By default, dbt will use Snowpark's _temporary_ or _anonymous_ stored procedures ([docs](https://docs.snowflake.com/en/sql-reference/sql/call-with.html)), which are faster and keep query history cleaner than named sprocs containing your model's compiled Python code. To disable this feature, set `use_anonymous_sproc: False` in your model configuration. - +**Docs:** ["Developer Guide: Snowpark Python"](https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html) + +#### Third-party Snowflake packages + +To use a third-party Snowflake package that isn't available in Snowflake Anaconda, upload your package by following [this example](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages#importing-packages-through-a-snowflake-stage), and then configure the `imports` setting in the dbt Python model to reference to the zip file in your Snowflake staging. + +Here’s a complete example configuration using a zip file, including using `imports` in a Python model: + +```python + +def model(dbt, session): + # Configure the model + dbt.config( + materialized="table", + imports=["@mystage/mycustompackage.zip"], # Specify the external package location + ) + + # Example data transformation using the imported package + # (Assuming `some_external_package` has a function we can call) + data = { + "name": ["Alice", "Bob", "Charlie"], + "score": [85, 90, 88] + } + df = pd.DataFrame(data) + + # Process data with the external package + df["adjusted_score"] = df["score"].apply(lambda x: some_external_package.adjust_score(x)) + + # Return the DataFrame as the model output + return df -```yml -# I asked Snowflake Support to enable this Private Preview feature, -# and now my dbt-py models run even faster! -models: - use_anonymous_sproc: True ``` - +For more information on using this configuration, refer to [Snowflake's documentation](https://community.snowflake.com/s/article/how-to-use-other-python-packages-in-snowpark) on uploading and using other python packages in Snowpark not published on Snowflake's Anaconda channel. -**Docs:** ["Developer Guide: Snowpark Python"](https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html) diff --git a/website/docs/docs/build/ratio-metrics.md b/website/docs/docs/build/ratio-metrics.md index fdaeb878450..a34dec29d71 100644 --- a/website/docs/docs/build/ratio-metrics.md +++ b/website/docs/docs/build/ratio-metrics.md @@ -10,17 +10,17 @@ Ratio allows you to create a ratio between two metrics. You simply specify a num The parameters, description, and type for ratio metrics are: -| Parameter | Description | Type | -| --------- | ----------- | ---- | -| `name` | The name of the metric. | Required | -| `description` | The description of the metric. | Optional | -| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required | -| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | -| `type_params` | The type parameters of the metric. | Required | -| `numerator` | The name of the metric used for the numerator, or structure of properties. | Required | -| `denominator` | The name of the metric used for the denominator, or structure of properties. | Required | -| `filter` | Optional filter for the numerator or denominator. | Optional | -| `alias` | Optional alias for the numerator or denominator. | Optional | +| Parameter | Description | Required | Type | +| --------- | ----------- | ---- | ---- | +| `name` | The name of the metric. | Required | String | +| `description` | The description of the metric. | Optional | String | +| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required | String | +| `label` | Defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | String | +| `type_params` | The type parameters of the metric. | Required | Dict | +| `numerator` | The name of the metric used for the numerator, or structure of properties. | Required | String or dict | +| `denominator` | The name of the metric used for the denominator, or structure of properties. | Required | String or dict | +| `filter` | Optional filter for the numerator or denominator. | Optional | String | +| `alias` | Optional alias for the numerator or denominator. | Optional | String | The following displays the complete specification for ratio metrics, along with an example. diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md index 609d7f1ff8d..5ff363dd44c 100644 --- a/website/docs/docs/build/semantic-models.md +++ b/website/docs/docs/build/semantic-models.md @@ -26,18 +26,18 @@ import SLCourses from '/snippets/\_sl-course.md'; Here we describe the Semantic model components with examples: -| Component | Description | Type | -| --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------- | -| [Name](#name) | Choose a unique name for the semantic model. Avoid using double underscores (\_\_) in the name as they're not supported. | Required | -| [Description](#description) | Includes important details in the description | Optional | -| [Model](#model) | Specifies the dbt model for the semantic model using the `ref` function | Required | -| [Defaults](#defaults) | The defaults for the model, currently only `agg_time_dimension` is supported. | Required | -| [Entities](#entities) | Uses the columns from entities as join keys and indicate their type as primary, foreign, or unique keys with the `type` parameter | Required | -| [Primary Entity](#primary-entity) | If a primary entity exists, this component is Optional. If the semantic model has no primary entity, then this property is required. | Optional | -| [Dimensions](#dimensions) | Different ways to group or slice data for a metric, they can be `time` or `categorical` | Required | -| [Measures](#measures) | Aggregations applied to columns in your data model. They can be the final metric or used as building blocks for more complex metrics | Optional | -| Label | The display name for your semantic model `node`, `dimension`, `entity`, and/or `measures` | Optional | -| `config` | Use the [`config`](/reference/resource-properties/config) property to specify configurations for your metric. Supports [`meta`](/reference/resource-configs/meta), [`group`](/reference/resource-configs/group), and [`enabled`](/reference/resource-configs/enabled) configs. | Optional | +| Component | Description | Required | Type | +| ------------ | ---------------- | -------- | -------- | +| [Name](#name) | Choose a unique name for the semantic model. Avoid using double underscores (\_\_) in the name as they're not supported. | Required | String | +| [Description](#description) | Includes important details in the description. | Optional | String | +| [Model](#model) | Specifies the dbt model for the semantic model using the `ref` function. | Required | String | +| [Defaults](#defaults) | The defaults for the model, currently only `agg_time_dimension` is supported. | Required | Dict | +| [Entities](#entities) | Uses the columns from entities as join keys and indicate their type as primary, foreign, or unique keys with the `type` parameter. | Required | List | +| [Primary Entity](#primary-entity) | If a primary entity exists, this component is Optional. If the semantic model has no primary entity, then this property is required. | Optional | String | +| [Dimensions](#dimensions) | Different ways to group or slice data for a metric, they can be `time` or `categorical`. | Required | List | +| [Measures](#measures) | Aggregations applied to columns in your data model. They can be the final metric or used as building blocks for more complex metrics. | Optional | List | +| [Label](#label) | The display name for your semantic model `node`, `dimension`, `entity`, and/or `measures`. | Optional | String | +| `config` | Use the [`config`](/reference/resource-properties/config) property to specify configurations for your metric. Supports [`meta`](/reference/resource-configs/meta), [`group`](/reference/resource-configs/group), and [`enabled`](/reference/resource-configs/enabled) configs. | Optional | Dict | ## Semantic models components diff --git a/website/docs/docs/build/simple.md b/website/docs/docs/build/simple.md index f57d498d290..2deb718d780 100644 --- a/website/docs/docs/build/simple.md +++ b/website/docs/docs/build/simple.md @@ -15,17 +15,19 @@ Simple metrics are metrics that directly reference a single measure, without any Note that we use the double colon (::) to indicate whether a parameter is nested within another parameter. So for example, `query_params::metrics` means the `metrics` parameter is nested under `query_params`. ::: -| Parameter | Description | Type | -| --------- | ----------- | ---- | -| `name` | The name of the metric. | Required | -| `description` | The description of the metric. | Optional | -| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required | -| `label` | Required string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | -| `type_params` | The type parameters of the metric. | Required | -| `measure` | A list of measure inputs | Required | -| `measure:name` | The measure you're referencing. | Required | -| `measure:fill_nulls_with` | Set the value in your metric definition instead of null (such as zero). | Optional | -| `measure:join_to_timespine` | Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. | Optional | +| Parameter | Description | Required | Type | +| --------- | ----------- | ---- | ---- | +| `name` | The name of the metric. | Required | String | +| `description` | The description of the metric. | Optional | String | +| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required | String | +| `label` | Defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). | Required | String | +| `type_params` | The type parameters of the metric. | Required | Dict | +| `measure` | A list of measure inputs. | Required | List | +| `measure:name` | The measure you're referencing. | Required | String | +| `measure:alias` | Optional [`alias`](/reference/resource-configs/alias) to rename the measure. | Optional | String | +| `measure:filter` | Optional `filter` applied to the measure. | Optional | String | +| `measure:fill_nulls_with` | Set the value in your metric definition instead of null (such as zero). | Optional | String | +| `measure:join_to_timespine` | Indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. | Optional | Boolean | The following displays the complete specification for simple metrics, along with an example. @@ -38,6 +40,8 @@ metrics: type_params: # Required measure: name: The name of your measure # Required + alias: The alias applied to the measure. # Optional + filter: The filter applied to the measure. # Optional fill_nulls_with: Set value instead of null (such as zero) # Optional join_to_timespine: true/false # Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. # Optional @@ -65,9 +69,11 @@ If you've already defined the measure using the `create_metric: true` parameter, name: customers # The measure you are creating a proxy of. fill_nulls_with: 0 join_to_timespine: true + alias: customer_count + filter: {{ Dimension('customer__customer_total') }} >= 20 - name: large_orders description: "Order with order values over 20." - type: SIMPLE + type: simple label: Large orders type_params: measure: diff --git a/website/docs/docs/build/snapshots.md b/website/docs/docs/build/snapshots.md index f5321aa626a..f72f1eb75de 100644 --- a/website/docs/docs/build/snapshots.md +++ b/website/docs/docs/build/snapshots.md @@ -10,8 +10,7 @@ id: "snapshots" * [Snapshot properties](/reference/snapshot-properties) * [`snapshot` command](/reference/commands/snapshot) - -### What are snapshots? +## What are snapshots? Analysts often need to "look back in time" at previous data states in their mutable tables. While some source data systems are built in a way that makes accessing historical data possible, this is not always the case. dbt provides a mechanism, **snapshots**, which records changes to a mutable over time. Snapshots implement [type-2 Slowly Changing Dimensions](https://en.wikipedia.org/wiki/Slowly_changing_dimension#Type_2:_add_new_row) over mutable source tables. These Slowly Changing Dimensions (or SCDs) identify how a row in a table changes over time. Imagine you have an `orders` table where the `status` field can be overwritten as the order is processed. @@ -36,16 +35,11 @@ This order is now in the "shipped" state, but we've lost the information about w ## Configuring snapshots -:::info Previewing or compiling snapshots in IDE not supported - -It is not possible to "preview data" or "compile sql" for snapshots in dbt Cloud. Instead, [run the `dbt snapshot` command](#how-snapshots-work) in the IDE. - -::: - - To configure snapshots in versions 1.8 and earlier, refer to [Configure snapshots in versions 1.8 and earlier](#configure-snapshots-in-versions-18-and-earlier). These versions use an older syntax where snapshots are defined within a snapshot block in a `.sql` file, typically located in your `snapshots` directory. -- Note that defining multiple resources in a single file can significantly slow down parsing and compilation. For faster and more efficient management, consider the updated snapshot YAML syntax, [available in Versionless](/docs/dbt-versions/versionless-cloud) or [dbt Core v1.9 and later](/docs/dbt-versions/core). +- Note that defining multiple resources in a single file can significantly slow down parsing and compilation. For faster and more efficient management, consider the updated snapshot YAML syntax, [available now in the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks) or [dbt Core v1.9 and later](/docs/dbt-versions/core). + - For more information on how to migrate from the legacy snapshot configurations to the updated snapshot YAML syntax, refer to [Snapshot configuration migration](/reference/snapshot-configs#snapshot-configuration-migration). @@ -60,6 +54,7 @@ Configure your snapshots in YAML files to tell dbt how to detect record changes. snapshots: - name: string relation: relation # source('my_source', 'my_table') or ref('my_model') + [description](/reference/resource-properties/description): markdown_string config: [database](/reference/resource-configs/database): string [schema](/reference/resource-configs/schema): string @@ -68,9 +63,9 @@ snapshots: [unique_key](/reference/resource-configs/unique_key): column_name_or_expression [check_cols](/reference/resource-configs/check_cols): [column_name] | all [updated_at](/reference/resource-configs/updated_at): column_name - [invalidate_hard_deletes](/reference/resource-configs/invalidate_hard_deletes): true | false [snapshot_meta_column_names](/reference/resource-configs/snapshot_meta_column_names): dictionary - + [dbt_valid_to_current](/reference/resource-configs/dbt_valid_to_current): string + [hard_deletes](/reference/resource-configs/hard-deletes): ignore | invalidate | new_record ``` @@ -83,17 +78,18 @@ The following table outlines the configurations available for snapshots: | [schema](/reference/resource-configs/schema) | Specify a custom schema for the snapshot | No | snapshots | | [alias](/reference/resource-configs/alias) | Specify an alias for the snapshot | No | your_custom_snapshot | | [strategy](/reference/resource-configs/strategy) | The snapshot strategy to use. Valid values: `timestamp` or `check` | Yes | timestamp | -| [unique_key](/reference/resource-configs/unique_key) | A column or expression for the record | Yes | id | +| [unique_key](/reference/resource-configs/unique_key) | A column(s) (string or array) or expression for the record | Yes | `id` or `[order_id, product_id]` | | [check_cols](/reference/resource-configs/check_cols) | If using the `check` strategy, then the columns to check | Only if using the `check` strategy | ["status"] | | [updated_at](/reference/resource-configs/updated_at) | If using the `timestamp` strategy, the timestamp column to compare | Only if using the `timestamp` strategy | updated_at | -| [invalidate_hard_deletes](/reference/resource-configs/invalidate_hard_deletes) | Find hard deleted records in source and set `dbt_valid_to` to current time if the record no longer exists | No | True | +| [dbt_valid_to_current](/reference/resource-configs/dbt_valid_to_current) | Set a custom indicator for the value of `dbt_valid_to` in current snapshot records (like a future date). By default, this value is `NULL`. When configured, dbt will use the specified value instead of `NULL` for `dbt_valid_to` for current records in the snapshot table.| No | string | | [snapshot_meta_column_names](/reference/resource-configs/snapshot_meta_column_names) | Customize the names of the snapshot meta fields | No | dictionary | +| [hard_deletes](/reference/resource-configs/hard-deletes) | Specify how to handle deleted rows from the source. Supported options are `ignore` (default), `invalidate` (replaces the legacy `invalidate_hard_deletes=true`), and `new_record`.| No | string | + - In versions prior to v1.9, the `target_schema` (required) and `target_database` (optional) configurations defined a single schema or database to build a snapshot across users and environment. This created problems when testing or developing a snapshot, as there was no clear separation between development and production environments. In v1.9, `target_schema` became optional, allowing snapshots to be environment-aware. By default, without `target_schema` or `target_database` defined, snapshots now use the `generate_schema_name` or `generate_database_name` macros to determine where to build. Developers can still set a custom location with [`schema`](/reference/resource-configs/schema) and [`database`](/reference/resource-configs/database) configs, consistent with other resource types. - A number of other configurations are also supported (for example, `tags` and `post-hook`). For the complete list, refer to [Snapshot configurations](/reference/snapshot-configs). - You can configure snapshots from both the `dbt_project.yml` file and a `config` block. For more information, refer to the [configuration docs](/reference/snapshot-configs). - ### Add a snapshot to your project To add a snapshot to your project follow these steps. For users on versions 1.8 and earlier, refer to [Configure snapshots in versions 1.8 and earlier](#configure-snapshots-in-versions-18-and-earlier). @@ -112,6 +108,7 @@ To add a snapshot to your project follow these steps. For users on versions 1.8 unique_key: id strategy: timestamp updated_at: updated_at + dbt_valid_to_current: "to_date('9999-12-31')" # Specifies that current records should have `dbt_valid_to` set to `'9999-12-31'` instead of `NULL`. ``` @@ -172,6 +169,15 @@ This strategy handles column additions and deletions better than the `check` str + + + +By default, `dbt_valid_to` is `NULL` for current records. However, if you set the [`dbt_valid_to_current` configuration](/reference/resource-configs/dbt_valid_to_current) (available in dbt Core v1.9+), `dbt_valid_to` will be set to your specified value (such as `9999-12-31`) for current records. + +This allows for straightforward date range filtering. + + + The unique key is used by dbt to match rows up, so it's extremely important to make sure this key is actually unique! If you're snapshotting a source, I'd recommend adding a uniqueness test to your source ([example](https://github.com/dbt-labs/jaffle_shop/blob/8e7c853c858018180bef1756ec93e193d9958c5b/models/staging/schema.yml#L26)). @@ -204,12 +210,18 @@ Snapshots can't be rebuilt. Because of this, it's a good idea to put snapshots i ### How snapshots work When you run the [`dbt snapshot` command](/reference/commands/snapshot): -* **On the first run:** dbt will create the initial snapshot table — this will be the result set of your `select` statement, with additional columns including `dbt_valid_from` and `dbt_valid_to`. All records will have a `dbt_valid_to = null`. +* **On the first run:** dbt will create the initial snapshot table — this will be the result set of your `select` statement, with additional columns including `dbt_valid_from` and `dbt_valid_to`. All records will have a `dbt_valid_to = null` or the value specified in [`dbt_valid_to_current`](/reference/resource-configs/dbt_valid_to_current) (available in dbt Core 1.9+) if configured. * **On subsequent runs:** dbt will check which records have changed or if any new records have been created: - - The `dbt_valid_to` column will be updated for any existing records that have changed - - The updated record and any new records will be inserted into the snapshot table. These records will now have `dbt_valid_to = null` + - The `dbt_valid_to` column will be updated for any existing records that have changed. + - The updated record and any new records will be inserted into the snapshot table. These records will now have `dbt_valid_to = null` or the value configured in `dbt_valid_to_current` (available in dbt Core v1.9+). + + -Note, these column names can be customized to your team or organizational conventions using the [snapshot_meta_column_names](#snapshot-meta-fields) config. +#### Note +- These column names can be customized to your team or organizational conventions using the [snapshot_meta_column_names](#snapshot-meta-fields) config. +- Use the `dbt_valid_to_current` config to set a custom indicator for the value of `dbt_valid_to` in current snapshot records (like a future date such as `9999-12-31`). By default, this value is `NULL`. When set, dbt will use this specified value instead of `NULL` for `dbt_valid_to` for current records in the snapshot table. +- Use the [`hard_deletes`](/reference/resource-configs/hard-deletes) config to track hard deletes by adding a new record when row become "deleted" in source. Supported options are `ignore`, `invalidate`, and `new_record`. + Snapshots can be referenced in downstream models the same way as referencing models — by using the [ref](/reference/dbt-jinja-functions/ref) function. @@ -286,7 +298,7 @@ The `check` snapshot strategy can be configured to track changes to _all_ column ::: -**Example Usage** +**Example usage** @@ -336,15 +348,64 @@ snapshots: ### Hard deletes (opt-in) + + +In dbt v1.9 and higher, the [`hard_deletes`](/reference/resource-configs/hard-deletes) config replaces the `invalidate_hard_deletes` config to give you more control on how to handle deleted rows from the source. The `hard_deletes` config is not a separate strategy but an additional opt-in feature that can be used with any snapshot strategy. + +The `hard_deletes` config has three options/fields: +| Field | Description | +| --------- | ----------- | +| `ignore` (default) | No action for deleted records. | +| `invalidate` | Behaves the same as the existing `invalidate_hard_deletes=true`, where deleted records are invalidated by setting `dbt_valid_to`. | +| `new_record` | Tracks deleted records as new rows using the `dbt_is_deleted` [meta field](#snapshot-meta-fields) when records are deleted.| + +import HardDeletes from '/snippets/_hard-deletes.md'; + + + +#### Example usage + + + +```yaml +snapshots: + - name: orders_snapshot_hard_delete + relation: source('jaffle_shop', 'orders') + config: + schema: snapshots + unique_key: id + strategy: timestamp + updated_at: updated_at + hard_deletes: new_record # options are: 'ignore', 'invalidate', or 'new_record' +``` + + + +In this example, the `hard_deletes: new_record` config will add a new row for deleted records with the `dbt_is_deleted` column set to `True`. +Any restored records are added as new rows with the `dbt_is_deleted` field set to `False`. + +The resulting table will look like this: + +| id | status | updated_at | dbt_valid_from | dbt_valid_to | dbt_is_deleted | +| -- | ------ | ---------- | -------------- | ------------ | -------------- | +| 1 | pending | 2024-01-01 10:47 | 2024-01-01 10:47 | 2024-01-01 11:05 | False | +| 1 | shipped | 2024-01-01 11:05 | 2024-01-01 11:05 | 2024-01-01 11:20 | False | +| 1 | deleted | 2024-01-01 11:20 | 2024-01-01 11:20 | 2024-01-01 12:00 | True | +| 1 | restored | 2024-01-01 12:00 | 2024-01-01 12:00 | | False | + + + + + Rows that are deleted from the source query are not invalidated by default. With the config option `invalidate_hard_deletes`, dbt can track rows that no longer exist. This is done by left joining the snapshot table with the source table, and filtering the rows that are still valid at that point, but no longer can be found in the source table. `dbt_valid_to` will be set to the current snapshot time. This configuration is not a different strategy as described above, but is an additional opt-in feature. It is not enabled by default since it alters the previous behavior. For this configuration to work with the `timestamp` strategy, the configured `updated_at` column must be of timestamp type. Otherwise, queries will fail due to mixing data types. -**Example Usage** +Note, in v1.9 and higher, the [`hard_deletes`](/reference/resource-configs/hard-deletes) config replaces the `invalidate_hard_deletes` config for better control over how to handle deleted rows from the source. - +#### Example usage @@ -370,61 +431,24 @@ For this configuration to work with the `timestamp` strategy, the configured `up - - - - -```yaml -snapshots: - - name: orders_snapshot_hard_delete - relation: source('jaffle_shop', 'orders') - config: - schema: snapshots - unique_key: id - strategy: timestamp - updated_at: updated_at - invalidate_hard_deletes: true -``` - - - - - -## Snapshot query best practices - -This section outlines some best practices for writing snapshot queries: - -- #### Snapshot source data - Your models should then select from these snapshots, treating them like regular data sources. As much as possible, snapshot your source data in its raw form and use downstream models to clean up the data - -- #### Use the `source` function in your query - This helps when understanding data lineage in your project. - -- #### Include as many columns as possible - In fact, go for `select *` if performance permits! Even if a column doesn't feel useful at the moment, it might be better to snapshot it in case it becomes useful – after all, you won't be able to recreate the column later. - -- #### Avoid joins in your snapshot query - Joins can make it difficult to build a reliable `updated_at` timestamp. Instead, snapshot the two tables separately, and join them in downstream models. - -- #### Limit the amount of transformation in your query - If you apply business logic in a snapshot query, and this logic changes in the future, it can be impossible (or, at least, very difficult) to apply the change in logic to your snapshots. - -Basically – keep your query as simple as possible! Some reasonable exceptions to these recommendations include: -* Selecting specific columns if the table is wide. -* Doing light transformation to get data into a reasonable shape, for example, unpacking a blob to flatten your source data into columns. - ## Snapshot meta-fields Snapshot tables will be created as a clone of your source dataset, plus some additional meta-fields*. -Starting in 1.9 or with [dbt Cloud Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless), these column names can be customized to your team or organizational conventions via the [`snapshot_meta_column_names`](/reference/resource-configs/snapshot_meta_column_names) config. +In dbt Core v1.9+ (or available sooner in [the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks)): +- These column names can be customized to your team or organizational conventions using the [`snapshot_meta_column_names`](/reference/resource-configs/snapshot_meta_column_names) config. +ess) +- Use the [`dbt_valid_to_current` config](/reference/resource-configs/dbt_valid_to_current) to set a custom indicator for the value of `dbt_valid_to` in current snapshot records (like a future date such as `9999-12-31`). By default, this value is `NULL`. When set, dbt will use this specified value instead of `NULL` for `dbt_valid_to` for current records in the snapshot table. +- Use the [`hard_deletes`](/reference/resource-configs/hard-deletes) config to track deleted records as new rows with the `dbt_is_deleted` meta field when using the `hard_deletes='new_record'` field. + | Field | Meaning | Usage | | -------------- | ------- | ----- | | dbt_valid_from | The timestamp when this snapshot row was first inserted | This column can be used to order the different "versions" of a record. | -| dbt_valid_to | The timestamp when this row became invalidated. | The most recent snapshot record will have `dbt_valid_to` set to `null`. | +| dbt_valid_to | The timestamp when this row became invalidated.
For current records, this is `NULL` by default or the value specified in `dbt_valid_to_current`. | The most recent snapshot record will have `dbt_valid_to` set to `NULL` or the specified value. | | dbt_scd_id | A unique key generated for each snapshotted record. | This is used internally by dbt | | dbt_updated_at | The updated_at timestamp of the source record when this snapshot row was inserted. | This is used internally by dbt | +| dbt_is_deleted | A boolean value indicating if the record has been deleted. `True` if deleted, `False` otherwise. | Added when `hard_deletes='new_record'` is configured. This is used internally by dbt | *The timestamps used for each column are subtly different depending on the strategy you use: @@ -458,6 +482,15 @@ Snapshot results (note that `11:30` is not used anywhere): | 1 | pending | 2024-01-01 10:47 | 2024-01-01 10:47 | 2024-01-01 11:05 | 2024-01-01 10:47 | | 1 | shipped | 2024-01-01 11:05 | 2024-01-01 11:05 | | 2024-01-01 11:05 | +Snapshot results with `hard_deletes='new_record'`: + +| id | status | updated_at | dbt_valid_from | dbt_valid_to | dbt_updated_at | dbt_is_deleted | +|----|---------|------------------|------------------|------------------|------------------|----------------| +| 1 | pending | 2024-01-01 10:47 | 2024-01-01 10:47 | 2024-01-01 11:05 | 2024-01-01 10:47 | False | +| 1 | shipped | 2024-01-01 11:05 | 2024-01-01 11:05 | 2024-01-01 11:20 | 2024-01-01 11:05 | False | +| 1 | deleted | 2024-01-01 11:20 | 2024-01-01 11:20 | | 2024-01-01 11:20 | True | + +
@@ -492,13 +525,23 @@ Snapshot results: | 1 | pending | 2024-01-01 11:00 | 2024-01-01 11:30 | 2024-01-01 11:00 | | 1 | shipped | 2024-01-01 11:30 | | 2024-01-01 11:30 | +Snapshot results with `hard_deletes='new_record'`: + +| id | status | dbt_valid_from | dbt_valid_to | dbt_updated_at | dbt_is_deleted | +|----|---------|------------------|------------------|------------------|----------------| +| 1 | pending | 2024-01-01 11:00 | 2024-01-01 11:30 | 2024-01-01 11:00 | False | +| 1 | shipped | 2024-01-01 11:30 | 2024-01-01 11:40 | 2024-01-01 11:30 | False | +| 1 | deleted | 2024-01-01 11:40 | | 2024-01-01 11:40 | True | + ## Configure snapshots in versions 1.8 and earlier -This section is for users on dbt versions 1.8 and earlier. To configure snapshots in versions 1.9 and later, refer to [Configuring snapshots](#configuring-snapshots). The latest versions use an updated snapshot configuration syntax that optimizes performance. +For information about configuring snapshots in dbt versions 1.8 and earlier, select **1.8** from the documentation version picker, and it will appear in this section. + +To configure snapshots in versions 1.9 and later, refer to [Configuring snapshots](#configuring-snapshots). The latest versions use an updated snapshot configuration syntax that optimizes performance. @@ -506,7 +549,8 @@ This section is for users on dbt versions 1.8 and earlier. To configure snapshot - In dbt versions 1.8 and earlier, snapshots are `select` statements, defined within a snapshot block in a `.sql` file (typically in your `snapshots` directory). You'll also need to configure your snapshot to tell dbt how to detect record changes. - The earlier dbt versions use an older syntax that allows for defining multiple resources in a single file. This syntax can significantly slow down parsing and compilation. -- For faster and more efficient management, consider[ upgrading to Versionless](/docs/dbt-versions/versionless-cloud) or the [latest version of dbt Core](/docs/dbt-versions/core), which introduces an updated snapshot configuration syntax that optimizes performance. +- For faster and more efficient management, consider [choosing the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks) or the [latest version of dbt Core](/docs/dbt-versions/core), which introduces an updated snapshot configuration syntax that optimizes performance. + - For more information on how to migrate from the legacy snapshot configurations to the updated snapshot YAML syntax, refer to [Snapshot configuration migration](/reference/snapshot-configs#snapshot-configuration-migration). The following example shows how to configure a snapshot: diff --git a/website/docs/docs/build/unit-tests.md b/website/docs/docs/build/unit-tests.md index 1d7143d7476..a81fc088de7 100644 --- a/website/docs/docs/build/unit-tests.md +++ b/website/docs/docs/build/unit-tests.md @@ -10,13 +10,13 @@ keywords: :::note -This functionality is only supported in dbt Core v1.8+ or accounts that have opted for a ["Versionless"](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) dbt Cloud experience. +Unit testing functionality is available in [dbt Cloud Release Tracks](/docs/dbt-versions/cloud-release-tracks) or dbt Core v1.8+ ::: Historically, dbt's test coverage was confined to [“data” tests](/docs/build/data-tests), assessing the quality of input data or resulting datasets' structure. However, these tests could only be executed _after_ building a model. -With dbt Core v1.8 and dbt Cloud environments that have gone versionless by selecting the **Versionless** option, we have introduced an additional type of test to dbt - unit tests. In software programming, unit tests validate small portions of your functional code, and they work much the same way here. Unit tests allow you to validate your SQL modeling logic on a small set of static inputs _before_ you materialize your full model in production. Unit tests enable test-driven development, benefiting developer efficiency and code reliability. +Starting in dbt Core v1.8, we have introduced an additional type of test to dbt - unit tests. In software programming, unit tests validate small portions of your functional code, and they work much the same way here. Unit tests allow you to validate your SQL modeling logic on a small set of static inputs _before_ you materialize your full model in production. Unit tests enable test-driven development, benefiting developer efficiency and code reliability. ## Before you begin @@ -24,11 +24,14 @@ With dbt Core v1.8 and dbt Cloud environments that have gone versionless by sele - We currently only support adding unit tests to models in your _current_ project. - We currently _don't_ support unit testing models that use the [`materialized view`](/docs/build/materializations#materialized-view) materialization. - We currently _don't_ support unit testing models that use recursive SQL. -- You must specify all fields in a BigQuery STRUCT in a unit test. You cannot use only a subset of fields in a STRUCT. - If your model has multiple versions, by default the unit test will run on *all* versions of your model. Read [unit testing versioned models](/reference/resource-properties/unit-testing-versions) for more information. -- Unit tests must be defined in a YML file in your `models/` directory. -- Table names must be [aliased](/docs/build/custom-aliases) in order to unit test `join` logic. -- Redshift customers need to be aware of a [limitation when building unit tests](/reference/resource-configs/redshift-configs#unit-test-limitations) that requires a workaround. +- Unit tests must be defined in a YML file in your [`models/` directory](/reference/project-configs/model-paths). +- Table names must be aliased in order to unit test `join` logic. +- Include all [`ref`](/reference/dbt-jinja-functions/ref) or [`source`](/reference/dbt-jinja-functions/source) model references in the unit test configuration as `input`s to avoid "node not found" errors during compilation. + +#### Adapter-specific caveats +- You must specify all fields in a BigQuery `STRUCT` in a unit test. You cannot use only a subset of fields in a `STRUCT`. +- Redshift customers need to be aware of a [limitation when building unit tests](/reference/resource-configs/redshift-configs#unit-test-limitations) that requires a workaround. Read the [reference doc](/reference/resource-properties/unit-tests) for more details about formatting your unit tests. diff --git a/website/docs/docs/cloud-integrations/configure-auto-exposures.md b/website/docs/docs/cloud-integrations/configure-auto-exposures.md index 42e36e572b3..2bb09573221 100644 --- a/website/docs/docs/cloud-integrations/configure-auto-exposures.md +++ b/website/docs/docs/cloud-integrations/configure-auto-exposures.md @@ -20,11 +20,13 @@ Auto-exposures help data teams optimize their efficiency and ensure data quality To access the features, you should meet the following: -1. Your environment and jobs are on [Versionless](/docs/dbt-versions/versionless-cloud) dbt. +1. Your environment and jobs are on a supported [release track](/docs/dbt-versions/cloud-release-tracks) dbt. 2. You have a dbt Cloud account on the [Enterprise plan](https://www.getdbt.com/pricing/). 3. You have set up a [production](/docs/deploy/deploy-environments#set-as-production-environment) deployment environment for each project you want to explore, with at least one successful job run. 4. You have [admin permissions](/docs/cloud/manage-access/enterprise-permissions) in dbt Cloud to edit project settings or production environment settings. 5. Use Tableau as your BI tool and enable metadata permissions or work with an admin to do so. Compatible with Tableau Cloud or Tableau Server with the Metadata API enabled. + - If you're using Tableau Server, you need to [allowlist dbt Cloud's IP addresses](/docs/cloud/about-cloud/access-regions-ip-addresses) for your dbt Cloud region. + - Currently, you can only connect to a single Tableau site on the same server. ## Set up in Tableau @@ -59,8 +61,14 @@ To set up [personal access tokens (PATs)](https://help.tableau.com/current/serve 4. Select the collections you want to include for auto exposures. - dbt Cloud automatically imports and syncs any workbook within the selected collections. New additions to the collections will be added to the lineage in dbt Cloud during the next automatic sync (usually once per day). + + :::info + dbt Cloud automatically imports and syncs any workbook within the selected collections. New additions to the collections will be added to the lineage in dbt Cloud during the next sync (automatically once per day). + + dbt Cloud immediately starts a sync when you update the selected collections list, capturing new workbooks and removing irrelevant ones. + ::: + 5. Click **Save**. dbt Cloud imports everything in the collection(s) and you can continue to view them in Explorer. For more information on how to view and use auto-exposures, refer to [View auto-exposures from dbt Explorer](/docs/collaborate/auto-exposures) page. diff --git a/website/docs/docs/cloud-integrations/semantic-layer/tableau.md b/website/docs/docs/cloud-integrations/semantic-layer/tableau.md index 15a0a92cf39..1f6755c38fa 100644 --- a/website/docs/docs/cloud-integrations/semantic-layer/tableau.md +++ b/website/docs/docs/cloud-integrations/semantic-layer/tableau.md @@ -46,8 +46,8 @@ Alternatively, you can follow these steps to install the Connector: ## Using the integration 1. **Authentication** — Once you authenticate, the system will direct you to the data source page. -2. **Access all Semantic Layer Objects** — Use the "ALL" data source to access all the metrics, dimensions, and entities configured in your dbt Semantic Layer. Note that the "METRICS_AND_DIMENSIONS" data source has been deprecated and replaced by "ALL". -3. **Access saved queries** — You can optionally access individual [saved queries](/docs/build/saved-queries) that you've defined. These will also show up as unique data sources when you log in. +2. **Access all Semantic Layer Objects** — Use the "ALL" data source to access all the metrics, dimensions, and entities configured in your dbt Semantic Layer. Note that the "METRICS_AND_DIMENSIONS" data source has been deprecated and replaced by "ALL". Be sure to use a live connection since extracts are not supported at this time. +3. **Access saved queries** — You can optionally access individual [saved queries](/docs/build/saved-queries) that you've defined. These will also show up as unique data sources when you log in. 4. **Access worksheet** — From your data source selection, go directly to a worksheet in the bottom left-hand corner. 5. **Query metrics and dimensions** — Then, you'll find all the metrics, dimensions, and entities that are available to query on the left side of your window based on your selection. diff --git a/website/docs/docs/cloud-integrations/set-up-snowflake-native-app.md b/website/docs/docs/cloud-integrations/set-up-snowflake-native-app.md index 49e6f90e41f..ff151d4636e 100644 --- a/website/docs/docs/cloud-integrations/set-up-snowflake-native-app.md +++ b/website/docs/docs/cloud-integrations/set-up-snowflake-native-app.md @@ -45,7 +45,10 @@ The following are the prerequisites for dbt Cloud and Snowflake. Configure dbt Cloud and Snowflake Cortex to power the **Ask dbt** chatbot. 1. In dbt Cloud, browse to your Semantic Layer configurations. - 1. From the gear menu, select **Account settings**. In the left sidebar, select **Projects** and choose your dbt project from the project list. + + 1. Navigate to the left hand side panel and click your account name. From there, select **Account settings**. + 1. In the left sidebar, select **Projects** and choose your dbt project from the project list. + 1. In the **Project details** panel, click the **Edit Semantic Layer Configuration** link (which is below the **GraphQL URL** option). 1. In the **Semantic Layer Configuration Details** panel, identify the Snowflake credentials (which you'll use to access Snowflake Cortex) and the environment against which the Semantic Layer is run. Save the username, role, and the environment in a temporary location to use later on. @@ -67,7 +70,7 @@ Configure dbt Cloud and Snowflake Cortex to power the **Ask dbt** chatbot. ## Configure dbt Cloud Collect the following pieces of information from dbt Cloud to set up the application. -1. From the gear menu in dbt Cloud, select **Account settings**. In the left sidebar, select **API tokens > Service tokens**. Create a service token with access to all the projects you want to access in the dbt Snowflake Native App. Grant these permission sets: +1. Navigate to the left-hand side panel and click your account name. From there, select **Account settings**. Then click **API tokens > Service tokens**. Create a service token with access to all the projects you want to access in the dbt Snowflake Native App. Grant these permission sets: - **Manage marketplace apps** - **Job Admin** - **Metadata Only** diff --git a/website/docs/docs/cloud/about-cloud-develop-defer.md b/website/docs/docs/cloud/about-cloud-develop-defer.md index fc55edf8a38..ea059ed3e27 100644 --- a/website/docs/docs/cloud/about-cloud-develop-defer.md +++ b/website/docs/docs/cloud/about-cloud-develop-defer.md @@ -13,11 +13,13 @@ Both the dbt Cloud IDE and the dbt Cloud CLI enable users to natively defer to p -By default, dbt follows these rules: +When using `--defer`, dbt Cloud will follow this order of execution for resolving the `{{ ref() }}` functions. -- dbt uses the production locations of parent models to resolve `{{ ref() }}` functions, based on metadata from the production environment. -- If a development version of a deferred model exists, dbt preferentially uses the development database location when resolving the reference. -- Passing the [`--favor-state`](/reference/node-selection/defer#favor-state) flag overrides the default behavior and _always_ resolve refs using production metadata, regardless of the presence of a development relation. +1. If a development version of a deferred relation exists, dbt preferentially uses the development database location when resolving the reference. +2. If a development version doesn't exist, dbt uses the staging locations of parent relations based on metadata from the staging environment. +3. If both a development and staging version doesn't exist, dbt uses the production locations of parent relations based on metadata from the production environment. + +**Note:** Passing the `--favor-state` flag will always resolve refs using production metadata, regardless of the presence of a development relation, skipping step #1. For a clean slate, it's a good practice to drop the development schema at the start and end of your development cycle. diff --git a/website/docs/docs/cloud/about-cloud/about-dbt-cloud.md b/website/docs/docs/cloud/about-cloud/about-dbt-cloud.md index d7afd424fc4..08bbcb94c3b 100644 --- a/website/docs/docs/cloud/about-cloud/about-dbt-cloud.md +++ b/website/docs/docs/cloud/about-cloud/about-dbt-cloud.md @@ -28,6 +28,12 @@ dbt Cloud's [flexible plans](https://www.getdbt.com/pricing/) and features make link="/docs/cloud/dbt-cloud-ide/develop-in-the-cloud" icon="dbt-bit"/> + + **Cell based:** ACCOUNT_PREFIX.us1.dbt.com | 52.45.144.63
54.81.134.249
52.22.161.231
52.3.77.232
3.214.191.130
34.233.79.135 | ✅ | ✅ | ✅ | +| North America [^1] | Azure
East US 2 (Virginia) | **Cell based:** ACCOUNT_PREFIX.us2.dbt.com | 20.10.67.192/26 | ❌ | ❌ | ✅ | | EMEA [^1] | AWS eu-central-1 (Frankfurt) | emea.dbt.com | 3.123.45.39
3.126.140.248
3.72.153.148 | ❌ | ❌ | ✅ | | EMEA [^1] | Azure
North Europe (Ireland) | **Cell based:** ACCOUNT_PREFIX.eu2.dbt.com | 20.13.190.192/26 | ❌ | ❌ | ✅ | | APAC [^1] | AWS ap-southeast-2 (Sydney)| au.dbt.com | 52.65.89.235
3.106.40.33
13.239.155.206
| ❌ | ❌ | ✅ | @@ -45,7 +46,7 @@ dbt Cloud, like many cloud services, relies on underlying AWS cloud infrastructu * Dynamic IP addresses — dbt Cloud infrastructure uses Amazon Web Services (AWS). dbt Cloud offers static URLs for streamlined access, but the dynamic nature of cloud services means the underlying IP addresses change occasionally. AWS manages the IP ranges and may change them according to their operational and security needs. -* Using hostnames for consistent access — To ensure uninterrupted access, we recommend that you dbt Cloud services using hostnames. Hostnames provide a consistent reference point, regardless of any changes in underlying IP addresses. We are aligning with an industry-standard practice employed by organizations such as Snowflake. +* Using hostnames for consistent access — To ensure uninterrupted access, we recommend that you use dbt Cloud services using hostnames. Hostnames provide a consistent reference point, regardless of any changes in underlying IP addresses. We are aligning with an industry-standard practice employed by organizations such as Snowflake. * Optimizing VPN connections — You should integrate a proxy alongside VPN for users who leverage VPN connections. This strategy enables steady IP addresses for your connections, facilitating smooth traffic flow through the VPN and onward to dbt Cloud. By employing a proxy and a VPN, you can direct traffic through the VPN and then to dbt Cloud. It's crucial to set up the proxy if you need to integrate with additional services. diff --git a/website/docs/docs/cloud/about-develop-dbt.md b/website/docs/docs/cloud/about-develop-dbt.md index 9568d70bb27..33d12b89e0f 100644 --- a/website/docs/docs/cloud/about-develop-dbt.md +++ b/website/docs/docs/cloud/about-develop-dbt.md @@ -9,9 +9,9 @@ hide_table_of_contents: true Develop dbt projects using dbt Cloud, which offers a fast and reliable way to work on your dbt project. It runs dbt Core in a hosted (single or multi-tenant) environment. -You can develop in your browser using an integrated development environment (IDE) or in a dbt Cloud-powered command line interface (CLI). +You can develop in your browser using an integrated development environment (IDE), a dbt Cloud-powered command line interface (CLI), or visual editor. -
+
+ +

To get started, you'll need a [dbt Cloud](https://www.getdbt.com/signup) account and a developer seat. For a more comprehensive guide about developing in dbt, refer to the [quickstart guides](/docs/get-started-dbt). diff --git a/website/docs/docs/cloud/account-integrations.md b/website/docs/docs/cloud/account-integrations.md new file mode 100644 index 00000000000..e5ff42cb900 --- /dev/null +++ b/website/docs/docs/cloud/account-integrations.md @@ -0,0 +1,103 @@ +--- +title: "Account integrations in dbt Cloud" +sidebar_label: "Account integrations" +description: "Learn how to configure account integrations for your dbt Cloud account." +--- + +The following sections describe the different **Account integrations** available from your dbt Cloud account under the account **Settings** section. + + + +## Git integrations + +Connect your dbt Cloud account to your Git provider to enable dbt Cloud users to authenticate your personal accounts. dbt Cloud will perform Git actions on behalf of your authenticated self, against repositories to which you have access according to your Git provider permissions. + +To configure a Git account integration: +1. Navigate to **Account settings** in the side menu. +2. Under the **Settings** section, click on **Integrations**. +3. Click on the Git provider from the list and select the **Pencil** icon to the right of the provider. +4. dbt Cloud [natively connects](/docs/cloud/git/git-configuration-in-dbt-cloud) to the following Git providers: + + - [GitHub](/docs/cloud/git/connect-github) + - [GitLab](/docs/cloud/git/connect-gitlab) + - [Azure DevOps](/docs/cloud/git/connect-azure-devops) + +You can connect your dbt Cloud account to additional Git providers by importing a git repository from any valid git URL. Refer to [Import a git repository](/docs/cloud/git/import-a-project-by-git-url) for more information. + + + +## OAuth integrations + +Connect your dbt Cloud account to an OAuth provider that are integrated with dbt Cloud. + +To configure an OAuth account integration: +1. Navigate to **Account settings** in the side menu. +2. Under the **Settings** section, click on **Integrations**. +3. Under **OAuth**, and click on **Link** to connect your Slack account. +4. For custom OAuth providers, under **Custom OAuth integrations**, click on **Add integration** and select the OAuth provider from the list. Fill in the required fields and click **Save**. + + + +## AI integrations + +Once AI features have been [enabled](/docs/cloud/enable-dbt-copilot#enable-dbt-copilot), you can use dbt Labs' AI integration or bring-your-own provider to support AI-powered dbt Cloud features like [dbt Copilot](/docs/cloud/dbt-copilot) and [Ask dbt](/docs/cloud-integrations/snowflake-native-app) (both available on [dbt Cloud Enterprise plans](https://www.getdbt.com/pricing)). + +dbt Cloud supports AI integrations for dbt Labs-managed OpenAI keys, Self-managed OpenAI keys, or Self-managed Azure OpenAI keys . + +Note, if you bring-your-own provider, you will incur API calls and associated charges for features used in dbt Cloud. + +:::info +dbt Cloud's AI is optimized for OpenAIs gpt-4o. Using other models can affect performance and accuracy, and functionality with other models isn't guaranteed. +::: + +To configure the AI integration in your dbt Cloud account, a dbt Cloud admin can perform the following steps: +1. Navigate to **Account settings** in the side menu. +2. Select **Integrations** and scroll to the **AI** section. +3. Click on the **Pencil** icon to the right of **OpenAI** to configure the AI integration. + +4. Configure the AI integration for either **dbt Labs OpenAI**, **OpenAI**, or **Azure OpenAI**. + + + + + 1. Select the toggle for **dbt Labs** to use dbt Labs' managed OpenAI key. + 2. Click **Save**. + + + + + + + 1. Select the toggle for **OpenAI** to use your own OpenAI key. + 2. Enter the API key. + 3. Click **Save**. + + + + + + To learn about deploying your own OpenAI model on Azure, refer to [Deploy models on Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-openai). Configure credentials for your Azure OpenAI deployment in dbt Cloud in the following two ways: + - [From a Target URI](#from-a-target-uri) + - [Manually providing the credentials](#manually-providing-the-credentials) + + #### From a Target URI + + 1. Locate your Azure OpenAI deployment URI in your Azure Deployment details page. + 2. In the dbt Cloud **Azure OpenAI** section, select the tab **From Target URI**. + 3. Paste the URI into the **Target URI** field. + 4. Enter your Azure OpenAI API key. + 5. Verify the **Endpoint**, **API Version**, and **Deployment Name** are correct. + 6. Click **Save**. + + + #### Manually providing the credentials + + 1. Locate your Azure OpenAI configuration in your Azure Deployment details page. + 2. In the dbt Cloud **Azure OpenAI** section, select the tab **Manual Input**. + 2. Enter your Azure OpenAI API key. + 3. Enter the **Endpoint**, **API Version**, and **Deployment Name**. + 4. Click **Save**. + + + + diff --git a/website/docs/docs/cloud/billing.md b/website/docs/docs/cloud/billing.md index ad0834c6c98..2c80648d1f9 100644 --- a/website/docs/docs/cloud/billing.md +++ b/website/docs/docs/cloud/billing.md @@ -149,7 +149,7 @@ dbt Labs may institute use limits if reasonable use is exceeded. Additional feat ## Managing usage -From anywhere in the dbt Cloud account, click the **gear icon** and click **Account settings**. The **Billing** option will be on the left side menu under the **Account Settings** heading. Here, you can view individual available plans and the features provided for each. +From dbt Cloud, click on your account name in the left side menu and select **Account settings**. The **Billing** option will be on the left side menu under the **Settings** heading. Here, you can view individual available plans and the features provided for each. ### Usage notifications diff --git a/website/docs/docs/cloud/cloud-cli-installation.md b/website/docs/docs/cloud/cloud-cli-installation.md index 8a058cbb90f..a80f1a587e0 100644 --- a/website/docs/docs/cloud/cloud-cli-installation.md +++ b/website/docs/docs/cloud/cloud-cli-installation.md @@ -21,8 +21,6 @@ dbt commands are run against dbt Cloud's infrastructure and benefit from: ## Prerequisites The dbt Cloud CLI is available in all [deployment regions](/docs/cloud/about-cloud/access-regions-ip-addresses) and for both multi-tenant and single-tenant accounts. -- You are on dbt version 1.5 or higher. Alternatively, set it to [**Versionless**](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) to automatically stay up to date. - ## Install dbt Cloud CLI You can install the dbt Cloud CLI on the command line by using one of these methods. @@ -321,3 +319,10 @@ This alias will allow you to use the dbt-cloud command to invoke th If you've ran a dbt command and receive a Session occupied error, you can reattach to your existing session with dbt reattach and then press Control-C and choose to cancel the invocation. + + + + +The Cloud CLI allows only one command that writes to the data warehouse at a time. If you attempt to run multiple write commands simultaneously (for example, `dbt run` and `dbt build`), you will encounter a `stuck session` error. To resolve this, cancel the specific invocation by passing its ID to the cancel command. For more information, refer to [parallel execution](/reference/dbt-commands#parallel-execution). + + \ No newline at end of file diff --git a/website/docs/docs/cloud/configure-cloud-cli.md b/website/docs/docs/cloud/configure-cloud-cli.md index 2e0fc174517..5e0a285c5c5 100644 --- a/website/docs/docs/cloud/configure-cloud-cli.md +++ b/website/docs/docs/cloud/configure-cloud-cli.md @@ -104,9 +104,9 @@ With your repo recloned, you can add, edit, and sync files with your repo. To set environment variables in the dbt Cloud CLI for your dbt project: -1. Select the gear icon on the upper right of the page. -2. Then select **Profile Settings**, then **Credentials**. -3. Click on your project and scroll to the **Environment Variables** section. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. +2. Under the **Your profile** section, select **Credentials**. +3. Click on your project and scroll to the **Environment variables** section. 4. Click **Edit** on the lower right and then set the user-level environment variables. ## Use the dbt Cloud CLI diff --git a/website/docs/docs/cloud/connect-data-platform/about-connections.md b/website/docs/docs/cloud/connect-data-platform/about-connections.md index 89dd13808ec..6497e86de89 100644 --- a/website/docs/docs/cloud/connect-data-platform/about-connections.md +++ b/website/docs/docs/cloud/connect-data-platform/about-connections.md @@ -88,7 +88,7 @@ Please consider the following actions, as the steps you take will depend on the - Normalization - - Undertsand how new connections should be created to avoid local overrides. If you currently use extended attributes to override the warehouse instance in your production environment - you should instead create a new connection for that instance, and wire your production environment to it, removing the need for the local overrides + - Understand how new connections should be created to avoid local overrides. If you currently use extended attributes to override the warehouse instance in your production environment - you should instead create a new connection for that instance, and wire your production environment to it, removing the need for the local overrides - Create new connections, update relevant environments to target these connections, removing now unecessary local overrides (which may not be all of them!) - Test the new wiring by triggering jobs or starting IDE sessions diff --git a/website/docs/docs/cloud/connect-data-platform/connect-amazon-athena.md b/website/docs/docs/cloud/connect-data-platform/connect-amazon-athena.md index f1009f61274..e3645500b9e 100644 --- a/website/docs/docs/cloud/connect-data-platform/connect-amazon-athena.md +++ b/website/docs/docs/cloud/connect-data-platform/connect-amazon-athena.md @@ -7,7 +7,7 @@ sidebar_label: "Connect Amazon Athena" # Connect Amazon Athena -Your environment(s) must be on ["Versionless"](/docs/dbt-versions/versionless-cloud) to use the Amazon Athena connection. +Your environment(s) must be on a supported [release track](/docs/dbt-versions/cloud-release-tracks) to use the Amazon Athena connection. Connect dbt Cloud to Amazon's Athena interactive query service to build your dbt project. The following are the required and optional fields for configuring the Athena connection: diff --git a/website/docs/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb.md b/website/docs/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb.md index 4719095b87f..5be802cae77 100644 --- a/website/docs/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb.md +++ b/website/docs/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb.md @@ -118,7 +118,7 @@ Once the connection is saved, a public key will be generated and displayed for t To configure the SSH tunnel in dbt Cloud, you'll need to provide the hostname/IP of your bastion server, username, and port, of your choosing, that dbt Cloud will connect to. Review the following steps: - Verify the bastion server has its network security rules set up to accept connections from the [dbt Cloud IP addresses](/docs/cloud/about-cloud/access-regions-ip-addresses) on whatever port you configured. -- Set up the user account by using the bastion servers instance's CLI, The following example uses the username `dbtcloud:` +- Set up the user account by using the bastion servers instance's CLI, The following example uses the username `dbtcloud`: ```shell sudo groupadd dbtcloud diff --git a/website/docs/docs/cloud/connect-data-platform/connect-snowflake.md b/website/docs/docs/cloud/connect-data-platform/connect-snowflake.md index d8dd8dfec11..6b749ced186 100644 --- a/website/docs/docs/cloud/connect-data-platform/connect-snowflake.md +++ b/website/docs/docs/cloud/connect-data-platform/connect-snowflake.md @@ -5,6 +5,14 @@ description: "Configure Snowflake connection." sidebar_label: "Connect Snowflake" --- +:::note + +dbt Cloud connections and credentials inherit the permissions of the accounts configured. You can customize roles and associated permissions in Snowflake to fit your company's requirements and fine-tune access to database objects in your account. See [Snowflake permissions](/reference/database-permissions/snowflake-permissions) for more information about customizing roles in Snowflake. + +Refer to [Snowflake permissions](/reference/database-permissions/snowflake-permissions) for more information about customizing roles in Snowflake. + +::: + The following fields are required when creating a Snowflake connection | Field | Description | Examples | @@ -14,12 +22,9 @@ The following fields are required when creating a Snowflake connection | Database | The logical database to connect to and run queries against. | `analytics` | | Warehouse | The virtual warehouse to use for running queries. | `transforming` | - -**Note:** A crucial part of working with dbt atop Snowflake is ensuring that users (in development environments) and/or service accounts (in deployment to production environments) have the correct permissions to take actions on Snowflake! Here is documentation of some [example permissions to configure Snowflake access](/reference/database-permissions/snowflake-permissions). - ## Authentication methods -This section describes the different authentication methods available for connecting dbt Cloud to Snowflake. +This section describes the different authentication methods for connecting dbt Cloud to Snowflake. Configure Deployment environment (Production, Staging, General) credentials globally in the [**Connections**](/docs/deploy/deploy-environments#deployment-connection) area of **Account settings**. Individual users configure their development credentials in the [**Credentials**](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud#get-started-with-the-cloud-ide) area of their user profile. ### Username / Password diff --git a/website/docs/docs/cloud/connect-data-platform/connect-starburst-trino.md b/website/docs/docs/cloud/connect-data-platform/connect-starburst-trino.md index db0d3f61728..4c460f0d705 100644 --- a/website/docs/docs/cloud/connect-data-platform/connect-starburst-trino.md +++ b/website/docs/docs/cloud/connect-data-platform/connect-starburst-trino.md @@ -11,7 +11,7 @@ The following are the required fields for setting up a connection with a [Starbu | **Host** | The hostname of your cluster. Don't include the HTTP protocol prefix. | `mycluster.mydomain.com` | | **Port** | The port to connect to your cluster. By default, it's 443 for TLS enabled clusters. | `443` | | **User** | The username (of the account) to log in to your cluster. When connecting to Starburst Galaxy clusters, you must include the role of the user as a suffix to the username.

| Format for Starburst Enterprise or Trino depends on your configured authentication method.
Format for Starburst Galaxy:
  • `user.name@mydomain.com/role`
| -| **Password** | The user's password. | | +| **Password** | The user's password. | - | | **Database** | The name of a catalog in your cluster. | `example_catalog` | | **Schema** | The name of a schema that exists within the specified catalog.  | `example_schema` | diff --git a/website/docs/docs/cloud/connect-data-platform/connect-teradata.md b/website/docs/docs/cloud/connect-data-platform/connect-teradata.md index cf41814078b..8663a181645 100644 --- a/website/docs/docs/cloud/connect-data-platform/connect-teradata.md +++ b/website/docs/docs/cloud/connect-data-platform/connect-teradata.md @@ -7,7 +7,7 @@ sidebar_label: "Connect Teradata" # Connect Teradata -Your environment(s) must be on ["Versionless"](/docs/dbt-versions/versionless-cloud) to use the Teradata connection. +Your environment(s) must be on a supported [release track](/docs/dbt-versions/cloud-release-tracks) to use the Teradata connection. | Field | Description | Type | Required? | Example | | ----------------------------- | --------------------------------------------------------------------------------------------- | -------------- | --------- | ------- | diff --git a/website/docs/docs/cloud/connect-data-platform/connnect-bigquery.md b/website/docs/docs/cloud/connect-data-platform/connnect-bigquery.md index 1ce9712ab91..ffe7e468bd2 100644 --- a/website/docs/docs/cloud/connect-data-platform/connnect-bigquery.md +++ b/website/docs/docs/cloud/connect-data-platform/connnect-bigquery.md @@ -11,7 +11,12 @@ sidebar_label: "Connect BigQuery" :::info Uploading a service account JSON keyfile -While the fields in a BigQuery connection can be specified manually, we recommend uploading a service account keyfile to quickly and accurately configure a connection to BigQuery. +While the fields in a BigQuery connection can be specified manually, we recommend uploading a service account keyfile to quickly and accurately configure a connection to BigQuery. + +You can provide the JSON keyfile in one of two formats: + +- JSON keyfile upload — Upload the keyfile directly in its normal JSON format. +- Base64-encoded string — Provide the keyfile as a base64-encoded string. When you provide a base64-encoded string, dbt decodes it automatically and populates the necessary fields. ::: diff --git a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md index d14435a97e0..abd3c86d4a8 100644 --- a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md +++ b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md @@ -81,7 +81,7 @@ To configure your own linting rules: :::tip Configure dbtonic linting rules -Refer to the [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) to add the dbt code (or dbtonic) rules we use for our own projects: +Refer to the [Jaffle shop SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for dbt-specific (or dbtonic) linting rules we use for our own projects:
dbtonic config code example provided by dbt Labs @@ -231,3 +231,4 @@ To avoid this, break up your model into smaller models (files) so that they are - [User interface](/docs/cloud/dbt-cloud-ide/ide-user-interface) - [Keyboard shortcuts](/docs/cloud/dbt-cloud-ide/keyboard-shortcuts) +- [SQL linting in CI jobs](/docs/deploy/continuous-integration#sql-linting) diff --git a/website/docs/docs/cloud/enable-dbt-copilot.md b/website/docs/docs/cloud/enable-dbt-copilot.md index 07a9f6294da..2b954d1db5d 100644 --- a/website/docs/docs/cloud/enable-dbt-copilot.md +++ b/website/docs/docs/cloud/enable-dbt-copilot.md @@ -12,7 +12,7 @@ This page explains how to enable the dbt Copilot engine in dbt Cloud, leveraging - Available in the dbt Cloud IDE only. - Must have an active [dbt Cloud Enterprise account](https://www.getdbt.com/pricing). -- Development environment has been upgraded to ["Versionless"](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless). +- Development environment is on a supported [release track](/docs/dbt-versions/cloud-release-tracks) to receive ongoing updates. - By default, dbt Copilot deployments use a central OpenAI API key managed by dbt Labs. Alternatively, you can [provide your own OpenAI API key](#bringing-your-own-openai-api-key-byok). - Accept and sign legal agreements. Reach out to your Account team to begin this process. @@ -34,18 +34,13 @@ Note: To disable (only after enabled), repeat steps 1 to 3, toggle off in step 4 -### Bringing your own OpenAI API key (BYOK) +## Bringing your own OpenAI API key (BYOK) -Once AI features have been enabled, you can provide your organization's OpenAI API key. dbt Cloud will then leverage your OpenAI account and terms to power dbt CoPilot. This will incur billing charges to your organization from OpenAI for requests made by dbt CoPilot. +Once AI features have been enabled, you can provide your organization's OpenAI API key. dbt Cloud will then leverage your OpenAI account and terms to power dbt Copilot. This will incur billing charges to your organization from OpenAI for requests made by dbt Copilot. -Note that Azure OpenAI is not currently supported, but will be in the future. +Configure AI keys using: +- [dbt Labs-managed OpenAI API key](/docs/cloud/account-integrations?ai-integration=dbtlabs#ai-integrations) +- Your own [OpenAI API key](/docs/cloud/account-integrations?ai-integration=openai#ai-integrations) +- [Azure OpenAI](/docs/cloud/account-integrations?ai-integration=azure#ai-integrations) -A dbt Cloud admin can provide their API key by following these steps: - -1. Navigate to **Account settings** in the side menu. - -2. Find the **Settings** section and click on **Integrations**. - -3. Scroll to **AI** and select the toggle for **OpenAI** - -4. Enter your API key and click **Save**. \ No newline at end of file +For configuration details, see [Account integrations](/docs/cloud/account-integrations#ai-integrations). diff --git a/website/docs/docs/cloud/git/connect-azure-devops.md b/website/docs/docs/cloud/git/connect-azure-devops.md index f6c0ee634fc..f3bb07a12d0 100644 --- a/website/docs/docs/cloud/git/connect-azure-devops.md +++ b/website/docs/docs/cloud/git/connect-azure-devops.md @@ -4,6 +4,8 @@ id: "connect-azure-devops" pagination_next: "docs/cloud/git/setup-azure" --- +# Connect to Azure DevOps + diff --git a/website/docs/docs/cloud/git/connect-github.md b/website/docs/docs/cloud/git/connect-github.md index e2bf459275e..df5c6cb0728 100644 --- a/website/docs/docs/cloud/git/connect-github.md +++ b/website/docs/docs/cloud/git/connect-github.md @@ -25,19 +25,21 @@ Connecting your GitHub account to dbt Cloud provides convenience and another lay You can connect your dbt Cloud account to GitHub by installing the dbt Cloud application in your GitHub organization and providing access to the appropriate repositories. To connect your dbt Cloud account to your GitHub account: -1. Navigate to **Your Profile** settings by clicking the gear icon in the top right. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. -2. Select **Linked Accounts** from the left menu. +2. Select **Personal profile** under the **Your profile** section. - +3. Scroll down to **Linked accounts**. -3. In the **Linked Accounts** section, set up your GitHub account connection to dbt Cloud by clicking **Link** to the right of GitHub. This redirects you to your account on GitHub where you will be asked to install and configure the dbt Cloud application. + -4. Select the GitHub organization and repositories dbt Cloud should access. +4. In the **Linked accounts** section, set up your GitHub account connection to dbt Cloud by clicking **Link** to the right of GitHub. This redirects you to your account on GitHub where you will be asked to install and configure the dbt Cloud application. + +5. Select the GitHub organization and repositories dbt Cloud should access. -5. Assign the dbt Cloud GitHub App the following permissions: +6. Assign the dbt Cloud GitHub App the following permissions: - Read access to metadata - Read and write access to Checks - Read and write access to Commit statuses @@ -46,8 +48,8 @@ To connect your dbt Cloud account to your GitHub account: - Read and write access to Webhooks - Read and write access to Workflows -6. Once you grant access to the app, you will be redirected back to dbt Cloud and shown a linked account success state. You are now personally authenticated. -7. Ask your team members to individually authenticate by connecting their [personal GitHub profiles](#authenticate-your-personal-github-account). +7. Once you grant access to the app, you will be redirected back to dbt Cloud and shown a linked account success state. You are now personally authenticated. +8. Ask your team members to individually authenticate by connecting their [personal GitHub profiles](#authenticate-your-personal-github-account). ## Limiting repository access in GitHub If you are your GitHub organization owner, you can also configure the dbt Cloud GitHub application to have access to only select repositories. This configuration must be done in GitHub, but we provide an easy link in dbt Cloud to start this process. @@ -67,14 +69,16 @@ After the dbt Cloud administrator [sets up a connection](/docs/cloud/git/connect To connect a personal GitHub account: -1. Navigate to **Your Profile** settings by clicking the gear icon in the top right. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. + +2. Select **Personal profile** under the **Your profile** section. -2. Select **Linked Accounts** in the left menu. If your GitHub account is not connected, you’ll see "No connected account". +3. Scroll down to **Linked accounts**. If your GitHub account is not connected, you’ll see "No connected account". -3. Select **Link** to begin the setup process. You’ll be redirected to GitHub, and asked to authorize dbt Cloud in a grant screen. +4. Select **Link** to begin the setup process. You’ll be redirected to GitHub, and asked to authorize dbt Cloud in a grant screen. -4. Once you approve authorization, you will be redirected to dbt Cloud, and you should now see your connected account. +5. Once you approve authorization, you will be redirected to dbt Cloud, and you should now see your connected account. You can now use the dbt Cloud IDE or dbt Cloud CLI. diff --git a/website/docs/docs/cloud/git/connect-gitlab.md b/website/docs/docs/cloud/git/connect-gitlab.md index f68f09ae73d..40d84f7d164 100644 --- a/website/docs/docs/cloud/git/connect-gitlab.md +++ b/website/docs/docs/cloud/git/connect-gitlab.md @@ -18,11 +18,12 @@ The steps to integrate GitLab in dbt Cloud depend on your plan. If you are on: ## For dbt Cloud Developer and Team tiers To connect your GitLab account: -1. Navigate to Your Profile settings by clicking the gear icon in the top right. -2. Select **Linked Accounts** in the left menu. -3. Click **Link** to the right of your GitLab account. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. +2. Select **Personal profile** under the **Your profile** section. +3. Scroll down to **Linked accounts**. +4. Click **Link** to the right of your GitLab account. - + When you click **Link**, you will be redirected to GitLab and prompted to sign into your account. GitLab will then ask for your explicit authorization: @@ -60,8 +61,8 @@ In GitLab, when creating your Group Application, input the following: | ------ | ----- | | **Name** | dbt Cloud | | **Redirect URI** | `https://YOUR_ACCESS_URL/complete/gitlab` | -| **Confidential** | ✔️ | -| **Scopes** | ✔️ api | +| **Confidential** | ✅ | +| **Scopes** | ✅ api | Replace `YOUR_ACCESS_URL` with the [appropriate Access URL](/docs/cloud/about-cloud/access-regions-ip-addresses) for your region and plan. @@ -99,7 +100,13 @@ Once you've accepted, you should be redirected back to dbt Cloud, and your integ ### Personally authenticating with GitLab dbt Cloud developers on the Enterprise plan must each connect their GitLab profiles to dbt Cloud, as every developer's read / write access for the dbt repo is checked in the dbt Cloud IDE or dbt Cloud CLI. -To connect a personal GitLab account, dbt Cloud developers should navigate to Your Profile settings by clicking the gear icon in the top right, then select **Linked Accounts** in the left menu. +To connect a personal GitLab account: + +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. + +2. Select **Personal profile** under the **Your profile** section. + +3. Scroll down to **Linked accounts**. If your GitLab account is not connected, you’ll see "No connected account". Select **Link** to begin the setup process. You’ll be redirected to GitLab, and asked to authorize dbt Cloud in a grant screen. diff --git a/website/docs/docs/cloud/git/import-a-project-by-git-url.md b/website/docs/docs/cloud/git/import-a-project-by-git-url.md index 90c54dbb1b1..2b499b39cb7 100644 --- a/website/docs/docs/cloud/git/import-a-project-by-git-url.md +++ b/website/docs/docs/cloud/git/import-a-project-by-git-url.md @@ -14,8 +14,8 @@ You must use the `git@...` or `ssh:..`. version of your git URL, not the `https: After importing a project by Git URL, dbt Cloud will generate a Deploy Key for your repository. To find the deploy key in dbt Cloud: -1. Click the gear icon in the upper right-hand corner. -2. Click **Account Settings** --> **Projects** and select a project. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. +2. Go to **Projects** and select a project. 3. Click the **Repository** link to the repository details page. 4. Copy the key under the **Deploy Key** section. @@ -49,7 +49,7 @@ If you use GitLab, you can import your repo directly using [dbt Cloud's GitLab A - To add a deploy key to a GitLab account, navigate to the [SSH keys](https://gitlab.com/profile/keys) tab in the User Settings page of your GitLab account. - Next, paste in the deploy key generated by dbt Cloud for your repository. - After saving this SSH key, dbt Cloud will be able to read and write files in your GitLab repository. -- Refer to [Adding a read only deploy key in GitLab](https://docs.gitlab.com/ee/ssh/#per-repository-deploy-keys) +- Refer to [Adding a read only deploy key in GitLab](https://docs.gitlab.com/ee/user/project/deploy_keys/) diff --git a/website/docs/docs/cloud/manage-access/audit-log.md b/website/docs/docs/cloud/manage-access/audit-log.md index 4d07afe2cde..de52434be06 100644 --- a/website/docs/docs/cloud/manage-access/audit-log.md +++ b/website/docs/docs/cloud/manage-access/audit-log.md @@ -18,7 +18,7 @@ The dbt Cloud audit log stores all the events that occurred in your organization ## Accessing the audit log -To access the audit log, click the gear icon in the top right, then click **Audit Log**. +To access the audit log, click on your account name in the left side menu and select **Account settings**. @@ -62,7 +62,7 @@ The audit log supports various events for different objects in dbt Cloud. You wi | Auth Provider Changed | auth_provider.Changed | Authentication provider settings changed | | Credential Login Succeeded | auth.CredentialsLoginSucceeded | User successfully logged in with username and password | | SSO Login Failed | auth.SsoLoginFailed | User login via SSO failed | -| SSO Login Succeeded | auth.SsoLoginSucceeded | User successfully logged in via SSO +| SSO Login Succeeded | auth.SsoLoginSucceeded | User successfully logged in via SSO | ### Environment @@ -93,7 +93,7 @@ The audit log supports various events for different objects in dbt Cloud. You wi | ------------- | ----------------------------- | ------------------------------ | | Group Added | user_group.Added | New Group successfully created | | Group Changed | user_group.Changed | Group settings changed | -| Group Removed | user_group.Changed | Group successfully removed | +| Group Removed | user_group.Removed | Group successfully removed | ### User @@ -149,12 +149,65 @@ The audit log supports various events for different objects in dbt Cloud. You wi ### Credentials -| Event Name | Event Type | Description | -| -------------------------------- | ----------------------------- | -------------------------------- | +| Event Name | Event Type | Description | +| -------------------------------- | ----------------------------- | -----------------------| | Credentials Added to Project | credentials.Added | Project credentials added | | Credentials Changed in Project | credentials.Changed | Credentials changed in project | | Credentials Removed from Project | credentials.Removed | Credentials removed from project | + +### Git integration + +| Event Name | Event Type | Description | +| -------------------------------- | ----------------------------- | -----------------------| +| GitLab Application Changed | gitlab_application.changed | GitLab configuration in dbt Cloud changed | + +### Webhooks + +| Event Name | Event Type | Description | +| -------------------------------- | ----------------------------- | -----------------------| +| Webhook Subscriptions Added | webhook_subscription.added | New webhook configured in settings | +| Webhook Subscriptions Changed | webhook_subscription.changed | Existing webhook configuration altered | +| Webhook Subscriptions Removed | webhook_subscription.removed | Existing webhook deleted | + + +### Semantic Layer + +| Event Name | Event Type | Description | +| -------------------------------- | ----------------------------- | -----------------------| +| Semantic Layer Config Added | semantic_layer_config.added | Semantic Layer config added | +| Semantic Layer Config Changed | semantic_layer_config.changed | Semantic Layer config (not related to credentials) changed | +| Semantic Layer Config Removed | semantic_layer_config.removed | Semantic Layer config removed | +| Semantic Layer Credentials Added | semantic_layer_credentials.added | Semantic Layer credentials added | +| Semantic Layer Credentials Changed| semantic_layer_credentials.changed | Semantic Layer credentials changed. Does not trigger semantic_layer_config.changed| +| Semantic Layer Credentials Removed| semantic_layer_credentials.removed | Semantic Layer credentials removed | + +### Extended attributes + +| Event Name | Event Type | Description | +| -------------------------------- | ----------------------------- | -----------------------| +| Extended Attribute Added | extended_attributes.added | Extended attribute added to a project | +| Extended Attribute Changed | extended_attributes.changed | Extended attribute changed or removed | + + +### Account-scoped personal access token + +| Event Name | Event Type | Description | +| -------------------------------- | ----------------------------- | -----------------------| +| Account Scoped Personal Access Token Created | account_scoped_pat.created | An account-scoped PAT was created | +| Account Scoped Personal Access Token Deleted | account_scoped_pat.deleted | An account-scoped PAT was deleted | + +### IP restrictions + +| Event Name | Event Type | Description | +| -------------------------------- | ----------------------------- | -----------------------| +| IP Restrictions Toggled | ip_restrictions.toggled | IP restrictions feature enabled or disabled | +| IP Restrictions Rule Added | ip_restrictions.rule.added | IP restriction rule created | +| IP Restrictions Rule Changed | ip_restrictions.rule.changed | IP restriction rule edited | +| IP Restrictions Rule Removed | ip_restrictions.rule.removed | IP restriction rule deleted | + + + ## Searching the audit log You can search the audit log to find a specific event or actor, which is limited to the ones listed in [Events in audit log](#events-in-audit-log). The audit log successfully lists historical events spanning the last 90 days. You can search for an actor or event using the search bar, and then narrow your results using the time window. diff --git a/website/docs/docs/cloud/manage-access/auth0-migration.md b/website/docs/docs/cloud/manage-access/auth0-migration.md index b7bab836810..2f45ad7dcc8 100644 --- a/website/docs/docs/cloud/manage-access/auth0-migration.md +++ b/website/docs/docs/cloud/manage-access/auth0-migration.md @@ -5,22 +5,10 @@ sidebar: "SSO Auth0 Migration" description: "Required actions for migrating to Auth0 for SSO services on dbt Cloud." --- -:::note - -This migration is a feature of the dbt Cloud Enterprise plan. To learn more about an Enterprise plan, contact us at [sales@getdbt.com](mailto::sales@getdbt.com). - -For single-tenant Virtual Private Cloud, you should [email dbt Cloud Support](mailto::support@getdbt.com) to set up or update your SSO configuration. - -::: - dbt Labs is partnering with Auth0 to bring enhanced features to dbt Cloud's single sign-on (SSO) capabilities. Auth0 is an identity and access management (IAM) platform with advanced security features, and it will be leveraged by dbt Cloud. These changes will require some action from customers with SSO configured in dbt Cloud today, and this guide will outline the necessary changes for each environment. If you have not yet configured SSO in dbt Cloud, refer instead to our setup guides for [SAML](/docs/cloud/manage-access/set-up-sso-saml-2.0), [Okta](/docs/cloud/manage-access/set-up-sso-okta), [Google Workspace](/docs/cloud/manage-access/set-up-sso-google-workspace), or [Microsoft Entra ID (formerly Azure AD)](/docs/cloud/manage-access/set-up-sso-microsoft-entra-id) single sign-on services. -## Auth0 Multi-tenant URIs - - - ## Start the migration The Auth0 migration feature is being rolled out incrementally to customers who have SSO features already enabled. When the migration option has been enabled on your account, you will see **SSO Updates Available** on the right side of the menu bar, near the settings icon. diff --git a/website/docs/docs/cloud/manage-access/cloud-seats-and-users.md b/website/docs/docs/cloud/manage-access/cloud-seats-and-users.md index f814d58777a..5628314c922 100644 --- a/website/docs/docs/cloud/manage-access/cloud-seats-and-users.md +++ b/website/docs/docs/cloud/manage-access/cloud-seats-and-users.md @@ -49,7 +49,7 @@ The following tabs detail steps on how to modify your user license count: If you're on an Enterprise plan and have the correct [permissions](/docs/cloud/manage-access/enterprise-permissions), you can add or remove licenses by adjusting your user seat count. Note, an IT license does not count toward seat usage. -- To remove a user, go to **Account Settings** and select **Users**. +- To remove a user, click on your account name in the left side menu, click **Account settings** and select **Users**. - Select the user you want to remove, click **Edit**, and then **Delete**. - This action cannot be undone. However, you can re-invite the user with the same info if you deleted the user in error.
@@ -64,7 +64,7 @@ If you're on an Enterprise plan and have the correct [permissions](/docs/cloud/m If you're on a Team plan and have the correct [permissions](/docs/cloud/manage-access/self-service-permissions), you can add or remove developers. You'll need to make two changes: -- Adjust your developer user seat count, which manages the users invited to your dbt Cloud project. AND +- Adjust your developer user seat count, which manages the users invited to your dbt Cloud project. - Adjust your developer billing seat count, which manages the number of billable seats. @@ -75,7 +75,7 @@ You can add or remove developers by increasing or decreasing the number of users To add a user in dbt Cloud, you must be an account owner or have admin privileges. -1. From dbt Cloud, click the gear icon at the top right and select **Account Settings**. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. @@ -95,11 +95,11 @@ Great work! After completing those these steps, your dbt Cloud user count and bi To delete a user in dbt Cloud, you must be an account owner or have admin privileges. If the user has a `developer` license type, this will open up their seat for another user or allow the admins to lower the total number of seats. -1. From dbt Cloud, click the gear icon at the top right and select **Account Settings**. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. -2. In **Account Settings** and select **Users**. +2. In **Account Settings**, select **Users**. 3. Select the user you want to delete, then click **Edit**. 4. Click **Delete** in the bottom left. Click **Confirm Delete** to immediately delete the user without additional password prompts. This action cannot be undone. However, you can re-invite the user with the same information if the deletion was made in error. diff --git a/website/docs/docs/cloud/manage-access/environment-permissions-setup.md b/website/docs/docs/cloud/manage-access/environment-permissions-setup.md index 1a3f2724819..5b41477e456 100644 --- a/website/docs/docs/cloud/manage-access/environment-permissions-setup.md +++ b/website/docs/docs/cloud/manage-access/environment-permissions-setup.md @@ -15,7 +15,7 @@ Environment-level permissions are not the same as account-level [role-based acce In your dbt Cloud account: -1. Open the **gear menu** and select **Account settings**. From the left-side menu, select **Groups & Licenses**. While you can edit existing groups, we recommend not altering the default `Everyone`, `Member`, and `Owner` groups. +1. Click your account name, above your profile icon on the left side panel, then select **Account settings**. From there, select **Groups & Licenses**. While you can edit existing groups, we recommend not altering the default `Everyone`, `Member`, and `Owner` groups. diff --git a/website/docs/docs/cloud/manage-access/environment-permissions.md b/website/docs/docs/cloud/manage-access/environment-permissions.md index b99da64609c..20acfae51f7 100644 --- a/website/docs/docs/cloud/manage-access/environment-permissions.md +++ b/website/docs/docs/cloud/manage-access/environment-permissions.md @@ -17,8 +17,8 @@ Environment-level permissions give dbt Cloud admins more flexibility to protect - Environment-level permissions do not allow you to create custom roles and permissions for each resource type in dbt Cloud. - You can only select environment types, and can’t specify a particular environment within a project. -- You can't select specific resources within environments. dbt Cloud jobs, runs, and environment variables are all environment resources. - - For example, you can't specify that a user only has access to jobs but not environment variables. Access to a given environment gives the user access to everything within that environment. +- You can't select specific resources within environments. dbt Cloud jobs and runs are environment resources. + - For example, you can't specify that a user only has access to jobs but not runs. Access to a given environment gives the user access to everything within that environment. ## Environments and roles diff --git a/website/docs/docs/cloud/manage-access/external-oauth.md b/website/docs/docs/cloud/manage-access/external-oauth.md index deb23f36f09..380d0a3d1cc 100644 --- a/website/docs/docs/cloud/manage-access/external-oauth.md +++ b/website/docs/docs/cloud/manage-access/external-oauth.md @@ -267,3 +267,7 @@ app in Entra ID, click **Endpoints** and open the **Federation metadata document 6. `Application ID URI`: Copy the `Application ID URI` field from the resource server’s Overview screen. + +## FAQs + + diff --git a/website/docs/docs/cloud/manage-access/self-service-permissions.md b/website/docs/docs/cloud/manage-access/self-service-permissions.md index a5bdba825c2..6b326645d44 100644 --- a/website/docs/docs/cloud/manage-access/self-service-permissions.md +++ b/website/docs/docs/cloud/manage-access/self-service-permissions.md @@ -52,33 +52,33 @@ The following tables outline the access that users have if they are assigned a D | Account-level permission| Owner | Member | Read-only license| IT license | |:------------------------|:-----:|:------:|:----------------:|:------------:| -| Account settings | W | W | | W | -| Billing | W | | | W | -| Invitations | W | W | | W | -| Licenses | W | R | | W | -| Users | W | R | | W | -| Project (create) | W | W | | W | -| Connections | W | W | | W | -| Service tokens | W | | | W | -| Webhooks | W | W | | | +| Account settings | W | W | - | W | +| Billing | W | - | - | W | +| Invitations | W | W | - | W | +| Licenses | W | R | - | W | +| Users | W | R | - | W | +| Project (create) | W | W | - | W | +| Connections | W | W | - | W | +| Service tokens | W | - | - | W | +| Webhooks | W | W | - | - | #### Project permissions for account roles |Project-level permission | Owner | Member | Read-only | IT license | |:------------------------|:-----:|:-------:|:---------:|:----------:| -| Adapters | W | W | R | | -| Connections | W | W | R | | -| Credentials | W | W | R | | -| Custom env. variables | W | W | R | | -| Develop (IDE or dbt Cloud CLI)| W | W | | | -| Environments | W | W | R | | -| Jobs | W | W | R | | -| dbt Explorer | W | W | R | | -| Permissions | W | R | | | -| Profile | W | W | R | | -| Projects | W | W | R | | -| Repositories | W | W | R | | -| Runs | W | W | R | | -| Semantic Layer Config | W | W | R | | +| Adapters | W | W | R | - | +| Connections | W | W | R | - | +| Credentials | W | W | R | - | +| Custom env. variables | W | W | R | - | +| Develop (IDE or dbt Cloud CLI)| W | W | - | - | +| Environments | W | W | R | - | +| Jobs | W | W | R | - | +| dbt Explorer | W | W | R | - | +| Permissions | W | R | - | - | +| Profile | W | W | R | - | +| Projects | W | W | R | - | +| Repositories | W | W | R | - | +| Runs | W | W | R | - | +| Semantic Layer Config | W | W | R | - | diff --git a/website/docs/docs/cloud/manage-access/set-up-bigquery-oauth.md b/website/docs/docs/cloud/manage-access/set-up-bigquery-oauth.md index 9a356814111..e528e2ebc1f 100644 --- a/website/docs/docs/cloud/manage-access/set-up-bigquery-oauth.md +++ b/website/docs/docs/cloud/manage-access/set-up-bigquery-oauth.md @@ -25,13 +25,14 @@ To use BigQuery in the dbt Cloud IDE, all developers must: ### Locate the redirect URI value To get started, locate the connection's redirect URI for configuring BigQuery OAuth. To do so: - - Select the gear menu in the upper left corner and choose **Account settings** + - Navigate to your account name, above your profile icon on the left side panel + - Select **Account settings** from the menu - From the left sidebar, select **Projects** - Choose the project from the list - Select **Connection** to edit the connection details - Locate the **Redirect URI** field under the **OAuth 2.0 Settings** section. Copy this value to your clipboard to use later on. - + ### Creating a BigQuery OAuth 2.0 client ID and secret To get started, you need to create a client ID and secret for [authentication](https://cloud.google.com/bigquery/docs/authentication) with BigQuery. This client ID and secret will be stored in dbt Cloud to manage the OAuth connection between dbt Cloud users and BigQuery. @@ -64,10 +65,12 @@ Now that you have an OAuth app set up in BigQuery, you'll need to add the client ### Authenticating to BigQuery Once the BigQuery OAuth app is set up for a dbt Cloud project, each dbt Cloud user will need to authenticate with BigQuery in order to use the IDE. To do so: -- Select the gear menu in the upper left corner and choose **Profile settings** +- Navigate to your account name, above your profile icon on the left side panel +- Select **Account settings** from the menu - From the left sidebar, select **Credentials** - Choose the project from the list - Select **Authenticate BigQuery Account** + You will then be redirected to BigQuery and asked to approve the drive, cloud platform, and BigQuery scopes, unless the connection is less privileged. diff --git a/website/docs/docs/cloud/manage-access/set-up-databricks-oauth.md b/website/docs/docs/cloud/manage-access/set-up-databricks-oauth.md index e5c42c3fa59..067d51513b7 100644 --- a/website/docs/docs/cloud/manage-access/set-up-databricks-oauth.md +++ b/website/docs/docs/cloud/manage-access/set-up-databricks-oauth.md @@ -45,11 +45,11 @@ You can use the following table to set up the redirect URLs for your application ### Configure the Connection in dbt Cloud (dbt Cloud project admin) Now that you have an OAuth app set up in Databricks, you'll need to add the client ID and secret to dbt Cloud. To do so: - - go to Settings by clicking the gear in the top right. - - on the left, select **Projects** under **Account Settings** - - choose your project from the list - - select **Connection** to edit the connection details - - add the `OAuth Client ID` and `OAuth Client Secret` from the Databricks OAuth app under the **Optional Settings** section + - From dbt Cloud, click on your account name in the left side menu and select **Account settings** + - Select **Projects** from the menu + - Choose your project from the list + - Select **Connection** to edit the connection details + - Add the `OAuth Client ID` and `OAuth Client Secret` from the Databricks OAuth app under the **Optional Settings** section @@ -57,7 +57,8 @@ Now that you have an OAuth app set up in Databricks, you'll need to add the clie Once the Databricks connection via OAuth is set up for a dbt Cloud project, each dbt Cloud user will need to authenticate with Databricks in order to use the IDE. To do so: -- Click the gear icon at the top right and select **Profile settings**. +- From dbt Cloud, click on your account name in the left side menu and select **Account settings** +- Select **Profile settings**. - Select **Credentials**. - Choose your project from the list - Select `OAuth` as the authentication method, and click **Save** diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-microsoft-entra-id.md b/website/docs/docs/cloud/manage-access/set-up-sso-microsoft-entra-id.md index 4658141034c..81463cf9ee5 100644 --- a/website/docs/docs/cloud/manage-access/set-up-sso-microsoft-entra-id.md +++ b/website/docs/docs/cloud/manage-access/set-up-sso-microsoft-entra-id.md @@ -61,6 +61,13 @@ Depending on your Microsoft Entra ID settings, your App Registration page might ### Azure <-> dbt Cloud User and Group mapping +:::important + +There is a [limitation](https://learn.microsoft.com/en-us/entra/identity/hybrid/connect/how-to-connect-fed-group-claims#important-caveats-for-this-functionality) on the number of groups Azure will emit (capped at 150) via the SSO token, meaning if a user belongs to more than 150 groups, it will appear as though they belong to none. To prevent this, configure [group assignments](https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/assign-user-or-group-access-portal?pivots=portal) with the dbt Cloud app in Azure and set a [group claim](https://learn.microsoft.com/en-us/entra/identity/hybrid/connect/how-to-connect-fed-group-claims#add-group-claims-to-tokens-for-saml-applications-using-sso-configuration) so Azure emits only the relevant groups. + +::: + + The Azure users and groups you will create in the following steps are mapped to groups created in dbt Cloud based on the group name. Reference the docs on [enterprise permissions](enterprise-permissions) for additional information on how users, groups, and permission sets are configured in dbt Cloud. ### Adding users to an Enterprise application @@ -120,8 +127,9 @@ To complete setup, follow the steps below in the dbt Cloud application. ### Supplying credentials -25. Click the gear icon at the top right and select **Profile settings**. To the left, select **Single Sign On** under **Account Settings**. -26. Click the **Edit** button and supply the following SSO details: +25. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. +26. Click **Single sign-on** from the menu. +27. Click the **Edit** button and supply the following SSO details: | Field | Value | | ----- | ----- | diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md b/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md index 7083e7ac5f8..34c1a91fbee 100644 --- a/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md +++ b/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md @@ -16,7 +16,7 @@ Currently supported features include: This document details the steps to integrate dbt Cloud with an identity provider in order to configure Single Sign On and [role-based access control](/docs/cloud/manage-access/about-user-access#role-based-access-control). -## Auth0 Multi-tenant URIs +## Auth0 URIs diff --git a/website/docs/docs/cloud/manage-access/sso-overview.md b/website/docs/docs/cloud/manage-access/sso-overview.md index 6b6527df753..e922a073fc8 100644 --- a/website/docs/docs/cloud/manage-access/sso-overview.md +++ b/website/docs/docs/cloud/manage-access/sso-overview.md @@ -12,7 +12,7 @@ dbt Cloud supports JIT (Just-in-Time) provisioning and IdP-initiated login. You - You have a dbt Cloud account enrolled in the Enterprise plan. [Contact us](mailto:sales@getdbt.com) to learn more and enroll. -## Auth0 Multi-tenant URIs +## Auth0 URIs diff --git a/website/docs/docs/cloud/secure/databricks-privatelink.md b/website/docs/docs/cloud/secure/databricks-privatelink.md index d754f2b76c4..aaa6e0c6eb7 100644 --- a/website/docs/docs/cloud/secure/databricks-privatelink.md +++ b/website/docs/docs/cloud/secure/databricks-privatelink.md @@ -34,7 +34,7 @@ The following steps will walk you through the setup of a Databricks AWS PrivateL 1. Once dbt Cloud support has notified you that setup is complete, [register the VPC endpoint in Databricks](https://docs.databricks.com/administration-guide/cloud-configurations/aws/privatelink.html#step-3-register-privatelink-objects-and-attach-them-to-a-workspace) and attach it to the workspace: - [Register your VPC endpoint](https://docs.databricks.com/en/security/network/classic/vpc-endpoints.html) — Register the VPC endpoint using the VPC endpoint ID provided by dbt Support. - [Create a Private Access Settings object](https://docs.databricks.com/en/security/network/classic/private-access-settings.html) — Create a Private Access Settings (PAS) object with your desired public access settings, and setting Private Access Level to **Endpoint**. Choose the registered endpoint created in the previous step. - - [Create or update your workspace](https://docs.databricks.com/en/security/network/classic/privatelink.html#step-3d-create-or-update-the-workspace-front-end-back-end-or-both) — Create a workspace, or update your an existing workspace. Under **Advanced configurations → Private Link** choose the private access settings object created in the previous step. + - [Create or update your workspace](https://docs.databricks.com/en/security/network/classic/privatelink.html#step-3d-create-or-update-the-workspace-front-end-back-end-or-both) — Create a workspace, or update an existing workspace. Under **Advanced configurations → Private Link** choose the private access settings object created in the previous step. :::warning If using an existing Databricks workspace, all workloads running in the workspace need to be stopped to enable Private Link. Workloads also can't be started for another 20 minutes after making changes. From the [Databricks documentation](https://docs.databricks.com/en/security/network/classic/privatelink.html#step-3d-create-or-update-the-workspace-front-end-back-end-or-both): diff --git a/website/docs/docs/cloud/secure/snowflake-privatelink.md b/website/docs/docs/cloud/secure/snowflake-privatelink.md index b943791292f..dc0cb64ba31 100644 --- a/website/docs/docs/cloud/secure/snowflake-privatelink.md +++ b/website/docs/docs/cloud/secure/snowflake-privatelink.md @@ -97,12 +97,18 @@ Once dbt Cloud support completes the configuration, you can start creating new c 4. Configure the remaining data platform details. 5. Test your connection and save it. -## Enable the connection in Snowflake +### Enable the connection in Snowflake hosted on Azure + +:::note + +AWS private internal stages are not currently supported. + +::: To complete the setup, follow the remaining steps from the Snowflake setup guides. The instructions vary based on the platform: -- [Snowflake AWS PrivateLink](https://docs.snowflake.com/en/user-guide/admin-security-privatelink) - [Snowflake Azure Private Link](https://docs.snowflake.com/en/user-guide/privatelink-azure) +- [Azure private endpoints for internal stages](https://docs.snowflake.com/en/user-guide/private-internal-stages-azure) There are some nuances for each connection and you will need a Snowflake administrator. As the Snowflake administrator, call the `SYSTEM$AUTHORIZE_STAGE_PRIVATELINK_ACCESS` function using the privateEndpointResourceID value as the function argument. This authorizes access to the Snowflake internal stage through the private endpoint. @@ -110,14 +116,12 @@ There are some nuances for each connection and you will need a Snowflake adminis USE ROLE ACCOUNTADMIN; --- AWS PrivateLink -SELECT SYSTEMS$AUTHORIZE_STATE_PRIVATELINK_ACCESS ( `AWS VPC ID` ); - -- Azure Private Link -SELECT SYSTEMS$AUTHORIZE_STATE_PRIVATELINK_ACCESS ( `AZURE PRIVATE ENDPOINT RESOURCE ID` ); +SELECT SYSTEMS$AUTHORIZE_STAGE_PRIVATELINK_ACCESS ( `AZURE PRIVATE ENDPOINT RESOURCE ID` ); ``` + ## Configuring Network Policies If your organization uses [Snowflake Network Policies](https://docs.snowflake.com/en/user-guide/network-policies) to restrict access to your Snowflake account, you will need to add a network rule for dbt Cloud. diff --git a/website/docs/docs/cloud/use-visual-editor.md b/website/docs/docs/cloud/use-visual-editor.md new file mode 100644 index 00000000000..2ab6a5b82d1 --- /dev/null +++ b/website/docs/docs/cloud/use-visual-editor.md @@ -0,0 +1,83 @@ +--- +title: "Edit and create dbt models" +id: use-visual-editor +sidebar_label: "Edit and create dbt models" +description: "Access and use the visual editor to create or edit dbt models through a visual, drag-and-drop experience inside of dbt Cloud." +pagination_prev: "docs/cloud/visual-editor-interface" +--- + +# Edit and create dbt models + +

+Access and use the dbt Cloud visual editor to create or edit dbt models through a visual, drag-and-drop experience. Use the built-in AI for custom code generation in your development experience. +

+ +:::tip Beta feature +The visual editor provides users with a seamless and drag-and-drop experience inside of dbt Cloud. It's available in private beta for [dbt Cloud Enterprise accounts](https://www.getdbt.com/pricing). + +To join the private beta, [register your interest](https://docs.google.com/forms/d/e/1FAIpQLScPjRGyrtgfmdY919Pf3kgqI5E95xxPXz-8JoVruw-L9jVtxg/viewform) or reach out to your account team to begin this process. +::: + +## Prerequisites +- You have a [dbt Cloud Enterprise](https://www.getdbt.com/pricing) account +- You have a [developer license](/docs/cloud/manage-access/seats-and-users) with developer credentials set up +- You have an existing dbt Cloud project already created +- Your Development environment is on a supported [release track](/docs/dbt-versions/cloud-release-tracks) to receive ongoing updates. +- Have AI-powered features toggle enabled + +## Access visual editor + +Before accessing the editor, you should have a dbt Cloud project already set up. This includes a Git repository, data platform connection, environments, and developer credentials. If you don't have this set up, please contact your dbt Cloud Admin. + +To access the visual editor: +- Type in the following URL, replacing the ACCOUNT_ID and ENVIRONMENT_ID with your own account and environment ID: `https://ACCESS_URL/visual-editor/ACCOUNT_ID/env/ENVIRONMENT_ID/` + - The environment ID must have had runs that generated catalogs in it. + +- For example, if my region is North America multi-tenant, account ID is 10, environment ID with a generated catalog run is 100, my URL should be: + + - `https://cloud.getdbt.com/visual-editor/10/env/100/` + + + +## Create a model +To create a dbt SQL model, click on **Create a new model** and perform the following steps. Note that you can't create source models in the visual editor. This is because you need to have production run with sources already created. + +1. Drag an operator from the operator toolbar and drop it onto the canvas. +2. Click on the operator to open its configuration panel: + - **Model**: Select the model and columns you want to use. + - **Join**: Define the join conditions and choose columns from both tables. + - **Select**: Pick the columns you need from the model. + - **Aggregate**: Specify the aggregation functions and the columns they apply to. + - **Formula**: Add the formula to create a new column. Use the built-AI code generator to help generate SQL code by clicking on the question mark (?) icon. Enter your prompt and wait to see the results. + - **Filter**: Set the conditions to filter data. + - **Order**: Select the columns to sort by and the sort order. + - **Limit**: Set the maximum number of rows you want to return. +3. View the **Output** and **SQL Code** tabs. + - Each operator has an Output tab that allows you to preview the data from that configured node. + - The Code tab displays the SQL code generated by the node's configuration. Use this to see the SQL for your visual model config. +4. Connect the operators by using the connector by dragging your cursor between the operator's "+" start point and linking it to the other operators you want to connect to. This should create a connector line. + - Doing this allows the data to flow from the source table through various transformations you configured, to the final output. +5. Keep building your dbt model and ensure you confirm the out through the **Output** tab. + + + +## Edit an existing model +To edit an existing model, navigate to the Visual Editor, click on the **Get Started** button on the upper right, and click **Edit existing model**. This will allow you to select the model you'd like to edit. + + + +## Version control + +Testing and documenting your models is an important part of the development process. + +Stay tuned! Coming very soon, you'll be able to version control your dbt modes in the visual editor. This ensures you can track changes and revert to previous versions if needed. + + diff --git a/website/docs/docs/cloud/visual-editor-interface.md b/website/docs/docs/cloud/visual-editor-interface.md new file mode 100644 index 00000000000..16e5a038d0e --- /dev/null +++ b/website/docs/docs/cloud/visual-editor-interface.md @@ -0,0 +1,84 @@ +--- +title: "Navigate the interface" +id: visual-editor-interface +sidebar_label: "Navigate the interface" +description: "The visual editor interface contains an operator toolbar, operators, and a canvas to help you create dbt models through a seamless drag-and-drop experience in dbt Cloud." +pagination_next: "docs/cloud/use-visual-editor" +pagination_prev: "docs/cloud/visual-editor" + +--- + +# Navigate the interface + +

+The visual editor interface contains an operator toolbar, operators, canvas, built-in AI, and more to help you create dbt models through a seamless drag-and-drop experience in dbt Cloud. +

+ +:::tip Beta feature +The visual editor provides users with a seamless and visual, drag-and-drop experience inside dbt Cloud. It's available in private beta for [dbt Cloud Enterprise accounts](https://www.getdbt.com/pricing). + +To join the private beta, [register your interest](https://docs.google.com/forms/d/e/1FAIpQLScPjRGyrtgfmdY919Pf3kgqI5E95xxPXz-8JoVruw-L9jVtxg/viewform) or reach out to your account team to begin this process. +::: + +This page offers comprehensive definitions and terminology of user interface elements, allowing you to navigate the dbt Cloud visual editor landscape with ease. + +The visual editor interface is composed of: + +- **Operator toolbar** — Located at the top of the interface, the toolbar displays all the nodes available. Use the toggle on the left of the toolbar to display or hide it. +- **Operators** — perform specific transformations or configurations (such as model, join, aggregate, filter, and so on). Use connectors to link the operators and build a complete data transformation pipeline. +- **Canvas** — The main whiteboard space below the node toolbar. The canvas allows you to create or modify models through a sleek drag-and-drop experience. +- **Configuration panel** — Each operator has a configuration panel that opens when you click on it. The configuration panel allows you to configure the operator, review the current model, preview changes to the table, view the SQL code for the node, and delete the operator. + +## Operators + +The operator toolbar above the canvas contains the different transformation operators available to use. Use each operator to configure or perform specific tasks, like adding filters or joining models by dragging an operator onto the canvas. You can connect operators using the connector line, which allows you to form a complete dbt model for your data transformation. + + + +Here the following operators are available: +- **Model**: This represents a data model. Use this to select the source table and the columns you want to include. There are no limits to the number of models you can have in a session. +- **Join**: Join two models and configure the join conditions by selecting which columns to include from each table. Requires two inputs. For example, you might want to join both tables using the 'ID' column found in both tables. +- **Select**: Use this to 'select' specific columns from a table. +- **Aggregate**: Allows you to perform aggregations like GROUP, SUM, AVG, COUNT, and so on. +- **Formula**: Create new columns using custom SQL formulas. Use a built-in AI code generator to generate SQL by clicking the ? icon. For example, you can use the formula node to only extract the email domain and ask the AI code generator to help you write the SQL for that code extraction. +- **Filter**: Filter data based on conditions you set. +- **Order**: Sort data by specific columns. +- **Limit**: Limits the number of rows returned back. + +When you click on each operator, it opens a configuration panel. The configuration panel allows you to configure the operator, review the current model, preview changes to the model, view the SQL code for the node, and delete the operator. + + + +If you have any feedback on additional operators that you might need, we'd love to hear it! Please contact your dbt Labs account team and share your thoughts. + +## Canvas + +The visual editor has a sleek drag-and-drop canvas interface that allows you to create or modify dbt SQL models. It's like a digital whiteboard space that allows analysts to deliver trustworthy data. Use the canvas to: + +- Drag-and-drop operators to create and configure your model(s) +- Generate SQL code using the built-in AI generator +- Zoom in or out for better visualization +- Version-control your dbt models +- [Coming soon] Test and document your created models + + + +### Connector + +Connectors allow you to connect your operators to create dbt models. Once you've added operators to the canvas: +- Hover over the "+" sign next to the operator and click. +- Drag your cursor between the operator's "+" start point to the other node you want to connect to. This should create a connector line. +- As an example, to create a join, connect one operator to the "L" (Left) and the other to the "R" (Right). The endpoints are located to the left of the operator so you can easily drag the connectors to the endpoint. + + + +## Configuration panel +Each operator has a configuration side panel that opens when you click on it. The configuration panel allows you to configure the operator, review the current model, preview changes, view the SQL code for the operator, and delete the operator. + +The configuration side panel has the following: +- Configure tab — This section allows you to configure the operator to your specified requirements, such as using the built-in AI code generator to generate SQL. +- Input tab — This section allows you to view the data for the current source table. Not available for model operators. +- Output tab — This section allows you to preview the data for the modified source model. +- Code — This section allows you to view the underlying SQL code for the data transformation. + + diff --git a/website/docs/docs/cloud/visual-editor.md b/website/docs/docs/cloud/visual-editor.md new file mode 100644 index 00000000000..8dc9dfa2863 --- /dev/null +++ b/website/docs/docs/cloud/visual-editor.md @@ -0,0 +1,37 @@ +--- +title: "About the visual editor" +id: visual-editor +sidebar_label: "About the visual editor" +description: "The visual editor enables analysts to quickly create and visualize dbt models through a visual, drag-and-drop experience inside of dbt Cloud." +pagination_next: "docs/cloud/visual-editor-interface" +pagination_prev: null +--- + +# About the visual editor + +

+The dbt Cloud visual editor helps analysts quickly create, edit, and visualize dbt models through a visual, drag-and-drop experience and with a built-in AI for custom code generation. +

+ +:::tip Beta feature +The visual editor in dbt Cloud provides users with a seamless and visual, drag-and-drop experience inside dbt Cloud. It's available in private beta for [dbt Cloud Enterprise accounts](https://www.getdbt.com/pricing). + +To join the private beta, [register your interest](https://docs.google.com/forms/d/e/1FAIpQLScPjRGyrtgfmdY919Pf3kgqI5E95xxPXz-8JoVruw-L9jVtxg/viewform) or reach out to your account team to begin this process. +::: + +The visual editor allows organizations to enjoy the many benefits of code-driven development—such as increased precision, ease of debugging, and ease of validation — while retaining the flexibility to have different contributors develop wherever they are most comfortable. Users can also take advantage of built-in AI for custom code generation, making it an end-to-end frictionless experience. + +These models compile directly to SQL and are indistinguishable from other dbt models in your projects: +- Visual models are version-controlled in your backing Git provider. +- All models are accessible across projects in [dbt Mesh](/best-practices/how-we-mesh/mesh-1-intro). +- Models can be materialized into production through [dbt Cloud orchestration](/docs/deploy/deployments), or be built directly into a user's development schema. +- Integrate with [dbt Explorer](/docs/collaborate/explore-projects) and the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud). + + + +## Feedback + +Please note, always review AI-generated code and content as it may produce incorrect results. The visual editor features and/or functionality may be added or eliminated as part of the beta trial. + +To give feedback, please reach out to your dbt Labs account team. We appreciate your feedback and suggestions as we improve the visual editor. + diff --git a/website/docs/docs/collaborate/auto-exposures.md b/website/docs/docs/collaborate/auto-exposures.md index 9b25a2fb305..495906cee75 100644 --- a/website/docs/docs/collaborate/auto-exposures.md +++ b/website/docs/docs/collaborate/auto-exposures.md @@ -9,11 +9,16 @@ image: /img/docs/cloud-integrations/auto-exposures/explorer-lineage.jpg # Auto-exposures -As a data team, it’s critical that you have context into the downstream use cases and users of your data products. Auto-exposures integrates natively with Tableau (Power BI coming soon) and auto-generates downstream lineage in dbt Explorer for a richer experience. +As a data team, it’s critical that you have context into the downstream use cases and users of your data products. Auto-exposures integrate natively with Tableau (Power BI coming soon) and auto-generate downstream lineage in dbt Explorer for a richer experience. -Auto-exposures helps users understand how their models are used in downstream analytics tools to inform investments and reduce incidents — ultimately building trust and confidence in data products. It imports and auto-generates exposures based on Tableau dashboards, with user-defined curation. +Auto-exposures help users understand how their models are used in downstream analytics tools to inform investments and reduce incidents — ultimately building trust and confidence in data products. It imports and auto-generates exposures based on Tableau dashboards, with user-defined curation. -Auto-exposures is available on [Versionless](/docs/dbt-versions/versionless-cloud) and on [dbt Cloud Enterprise](https://www.getdbt.com/pricing/) plans. +## Supported plans +Auto-exposures is available on the [dbt Cloud Enterprise](https://www.getdbt.com/pricing/) plan. Currently, you can only connect to a single Tableau site on the same server. + +:::info Tableau Server +If you're using Tableau Server, you need to [allowlist dbt Cloud's IP addresses](/docs/cloud/about-cloud/access-regions-ip-addresses) for your dbt Cloud region. +::: For more information on how to set up auto-exposures, prerequisites, and more — refer to [configure auto-exposures in Tableau and dbt Cloud](/docs/cloud-integrations/configure-auto-exposures). diff --git a/website/docs/docs/collaborate/build-and-view-your-docs.md b/website/docs/docs/collaborate/build-and-view-your-docs.md index 06716a67674..1a16f034eff 100644 --- a/website/docs/docs/collaborate/build-and-view-your-docs.md +++ b/website/docs/docs/collaborate/build-and-view-your-docs.md @@ -24,7 +24,7 @@ To set up a job to generate docs: 1. In the top left, click **Deploy** and select **Jobs**. 2. Create a new job or select an existing job and click **Settings**. 3. Under **Execution Settings**, select **Generate docs on run** and click **Save**. - + *Note, for dbt Docs users you need to configure the job to generate docs when it runs, then manually link that job to your project. Proceed to [configure project documentation](#configure-project-documentation) so your project generates the documentation when this job runs.* @@ -51,12 +51,11 @@ dbt Docs, available on developer plans or dbt Core users, generates a website fr You configure project documentation to generate documentation when the job you set up in the previous section runs. In the project settings, specify the job that generates documentation artifacts for that project. Once you configure this setting, subsequent runs of the job will automatically include a step to generate documentation. -1. Click the gear icon in the top right. -2. Select **Account Settings**. -3. Navigate to **Projects** and select the project that needs documentation. -4. Click **Edit**. -5. Under **Artifacts**, select the job that should generate docs when it runs and click **Save**. - +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. +2. Navigate to **Projects** and select the project that needs documentation. +3. Click **Edit**. +4. Under **Artifacts**, select the job that should generate docs when it runs and click **Save**. + :::tip Use dbt Explorer for a richer documentation experience For a richer and more interactive experience, try out [dbt Explorer](/docs/collaborate/explore-projects), available on [Team or Enterprise plans](https://www.getdbt.com/pricing/). It includes map layers of your DAG, keyword search, interacts with the IDE, model performance, project recommendations, and more. diff --git a/website/docs/docs/collaborate/explore-projects.md b/website/docs/docs/collaborate/explore-projects.md index a4388a8696e..3780d100932 100644 --- a/website/docs/docs/collaborate/explore-projects.md +++ b/website/docs/docs/collaborate/explore-projects.md @@ -164,12 +164,12 @@ Under the the **Models** option, you can filter on model properties (access or m -Trust signal icons offer a quick, at-a-glance view of data health when browsing your models in dbt Explorer. These icons keep you informed on the status of your model's health using the indicators **Healthy**, **Caution**, **Degraded**, and **Unknown**. For accurate health data, ensure the resource is up-to-date and has had a recent job run. +Trust signal icons offer a quick, at-a-glance view of data health when browsing your resources in dbt Explorer. These icons keep you informed on the status of your resource's health using the indicators **Healthy**, **Caution**, **Degraded**, and **Unknown**. For accurate health data, ensure the resource is up-to-date and has had a recent job run. Supported resources are models, sources, and exposures. Each trust signal icon reflects key data health components, such as test success status, missing resource descriptions, absence of builds in 30-day windows, and more. To access trust signals: -- Use the search function or click on **Models** or **Sources** under the **Resource** tab. +- Use the search function or click on **Models**, **Sources** or **Exposures** under the **Resource** tab. - View the icons under the **Health** column. - Hover over or click the trust signal to see detailed information. - For sources, the trust signal also indicates the source freshness status. diff --git a/website/docs/docs/collaborate/govern/model-contracts.md b/website/docs/docs/collaborate/govern/model-contracts.md index d30024157c8..9b75e518719 100644 --- a/website/docs/docs/collaborate/govern/model-contracts.md +++ b/website/docs/docs/collaborate/govern/model-contracts.md @@ -205,13 +205,11 @@ At the same time, for models with many columns, we understand that this can mean When comparing to a previous project state, dbt will look for breaking changes that could impact downstream consumers. If breaking changes are detected, dbt will present a contract error. -Breaking changes include: -- Removing an existing column. -- Changing the `data_type` of an existing column. -- Removing or modifying one of the `constraints` on an existing column (dbt v1.6 or higher). -- Removing a contracted model by deleting, renaming, or disabling it (dbt v1.9 or higher). - - versioned models will raise an error. - - unversioned models will raise a warning. +import BreakingChanges from '/snippets/_versions-contracts.md'; -More details are available in the [contract reference](/reference/resource-configs/contract#detecting-breaking-changes). + +More details are available in the [contract reference](/reference/resource-configs/contract#detecting-breaking-changes). diff --git a/website/docs/docs/collaborate/govern/project-dependencies.md b/website/docs/docs/collaborate/govern/project-dependencies.md index 7813e25efcb..bbda99960cd 100644 --- a/website/docs/docs/collaborate/govern/project-dependencies.md +++ b/website/docs/docs/collaborate/govern/project-dependencies.md @@ -18,7 +18,6 @@ This year, dbt Labs is introducing an expanded notion of `dependencies` across m ## Prerequisites - Available in [dbt Cloud Enterprise](https://www.getdbt.com/pricing). If you have an Enterprise account, you can unlock these features by designating a [public model](/docs/collaborate/govern/model-access) and adding a [cross-project ref](#how-to-write-cross-project-ref). -- Use a supported version of dbt (v1.6 or newer or go versionless with "[Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless)") for both the upstream ("producer") project and the downstream ("consumer") project. - Define models in an upstream ("producer") project that are configured with [`access: public`](/reference/resource-configs/access). You need at least one successful job run after defining their `access`. - Define a deployment environment in the upstream ("producer") project [that is set to be your Production environment](/docs/deploy/deploy-environments#set-as-production-environment), and ensure it has at least one successful job run in that environment. - If the upstream project has a Staging environment, run a job in that Staging environment to ensure the downstream cross-project ref resolves. diff --git a/website/docs/docs/collaborate/model-query-history.md b/website/docs/docs/collaborate/model-query-history.md index 0180757f980..872a5a295da 100644 --- a/website/docs/docs/collaborate/model-query-history.md +++ b/website/docs/docs/collaborate/model-query-history.md @@ -13,12 +13,19 @@ Model query history allows you to: - Provides data teams insight, so they can focus their time and infrastructure spend on the worthwhile used data products. - Enable analysts to find the most popular models used by other people. -Model query history is powered by a single consumption query of the query log table in your data warehouse aggregated on a daily basis. It currently supports Snowflake and BigQuery only, with additional platforms coming soon. +Model query history is powered by a single consumption query of the query log table in your data warehouse aggregated on a daily basis. + + -:::info What is a consumption query? Consumption query is a metric of queries in your dbt project that has used the model in a given time. It filters down to `select` statements only to gauge model consumption and excludes dbt model build and test executions. So for example, if `model_super_santi` was queried 10 times in the past week, it would count as having 10 consumption queries for that particular time period. + + + +:::info Support for Snowflake (Enterprise tier or higher) and BigQuery + +Model query history for Snowflake users is **only available for Enterprise tier or higher**. The feature also supports BigQuery. Additional platforms coming soon. ::: ## Prerequisites @@ -29,6 +36,7 @@ To access the features, you should meet the following: 2. You have set up a [production](https://docs.getdbt.com/docs/deploy/deploy-environments#set-as-production-environment) deployment environment for each project you want to explore, with at least one successful job run. 3. You have [admin permissions](/docs/cloud/manage-access/enterprise-permissions) in dbt Cloud to edit project settings or production environment settings. 4. Use Snowflake or BigQuery as your data warehouse and can enable query history permissions or work with an admin to do so. Support for additional data platforms coming soon. + - For Snowflake users: You **must** have a Snowflake Enterprise tier or higher subscription. ## Enable query history in dbt Cloud diff --git a/website/docs/docs/core/connect-data-platform/mssql-setup.md b/website/docs/docs/core/connect-data-platform/mssql-setup.md index f2b17278df3..31fa93874cf 100644 --- a/website/docs/docs/core/connect-data-platform/mssql-setup.md +++ b/website/docs/docs/core/connect-data-platform/mssql-setup.md @@ -4,7 +4,7 @@ description: "Read this guide to learn about the Microsoft SQL Server warehouse id: "mssql-setup" meta: maintained_by: Community - authors: 'dbt-msft community (https://github.com/dbt-msft)' + authors: 'Mikael Ene & dbt-msft community (https://github.com/dbt-msft)' github_repo: 'dbt-msft/dbt-sqlserver' pypi_package: 'dbt-sqlserver' min_core_version: 'v0.14.0' diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md index ce3e8658045..4c00558d782 100644 --- a/website/docs/docs/core/connect-data-platform/redshift-setup.md +++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md @@ -31,7 +31,7 @@ import SetUpPages from '/snippets/_setup-pages-intro.md'; | `port` | 5439 | | | `dbname` | my_db | Database name| | `schema` | my_schema | Schema name| -| `connect_timeout` | `None` or 30 | Number of seconds before connection times out| +| `connect_timeout` | 30 | Number of seconds before connection times out. Default is `None`| | `sslmode` | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below| | `role` | None | Optional, user identifier of the current session| | `autocreate` | false | Optional, default false. Creates user if they do not exist | diff --git a/website/docs/docs/core/connect-data-platform/risingwave-setup.md b/website/docs/docs/core/connect-data-platform/risingwave-setup.md index 29ebc378c4e..320ba2a506d 100644 --- a/website/docs/docs/core/connect-data-platform/risingwave-setup.md +++ b/website/docs/docs/core/connect-data-platform/risingwave-setup.md @@ -28,7 +28,7 @@ import SetUpPages from '/snippets/_setup-pages-intro.md'; ## Connecting to RisingWave with dbt-risingwave -Before connecting to RisingWave, ensure that RisingWave is installed and running. For more information about how to get RisingWave up and running, see the [RisingWave quick start guide](https://docs.risingwave.com/docs/dev/get-started/). +Before connecting to RisingWave, ensure that RisingWave is installed and running. For more information about how to get RisingWave up and running, see the [RisingWave quick start guide](https://docs.risingwave.com/get-started/quickstart). To connect to RisingWave with dbt, you need to add a RisingWave profile to your dbt profile file (`~/.dbt/profiles.yml`). Below is an example RisingWave profile. Revise the field values when necessary. @@ -71,17 +71,17 @@ The dbt models for managing data transformations in RisingWave are similar to ty |Materializations| Supported|Notes| |----|----|----| -|`table` |Yes |Creates a [table](https://docs.risingwave.com/docs/dev/sql-create-table/). To use this materialization, add `{{ config(materialized='table') }}` to your model SQL files. | -|`view`|Yes | Creates a [view](https://docs.risingwave.com/docs/dev/sql-create-view/). To use this materialization, add `{{ config(materialized='view') }}` to your model SQL files. | -|`ephemeral`|Yes| This materialization uses [common table expressions](https://docs.risingwave.com/docs/dev/query-syntax-with-clause/) in RisingWave under the hood. To use this materialization, add `{{ config(materialized='ephemeral') }}` to your model SQL files.| +|`table` |Yes |Creates a [table](https://docs.risingwave.com/sql/commands/sql-create-table). To use this materialization, add `{{ config(materialized='table') }}` to your model SQL files. | +|`view`|Yes | Creates a [view](https://docs.risingwave.com/sql/commands/sql-create-view). To use this materialization, add `{{ config(materialized='view') }}` to your model SQL files. | +|`ephemeral`|Yes| This materialization uses [common table expressions](https://docs.risingwave.com/sql/query-syntax/with-clause) in RisingWave under the hood. To use this materialization, add `{{ config(materialized='ephemeral') }}` to your model SQL files.| |`materializedview`| To be deprecated. |It is available only for backward compatibility purposes (for v1.5.1 of the dbt-risingwave adapter plugin). If you are using v1.6.0 and later versions of the dbt-risingwave adapter plugin, use `materialized_view` instead.| -|`materialized_view`| Yes| Creates a [materialized view](https://docs.risingwave.com/docs/dev/sql-create-mv/). This materialization corresponds the `incremental` one in dbt. To use this materialization, add `{{ config(materialized='materialized_view') }}` to your model SQL files.| +|`materialized_view`| Yes| Creates a [materialized view](https://docs.risingwave.com/sql/commands/sql-create-mv). This materialization corresponds the `incremental` one in dbt. To use this materialization, add `{{ config(materialized='materialized_view') }}` to your model SQL files.| | `incremental`|No|Please use `materialized_view` instead. Since RisingWave is designed to use materialized view to manage data transformation in an incremental way, you can just use the `materialized_view` materialization.| -|`source`| Yes| Creates a [source](https://docs.risingwave.com/docs/dev/sql-create-source/). To use this materialization, add \{\{ config(materialized='source') \}\} to your model SQL files. You need to provide your create source statement as a whole in this model. See [Example model files](https://docs.risingwave.com/docs/dev/use-dbt/#example-model-files) for details.| -|`table_with_connector`| Yes| Creates a table with connector settings. In RisingWave, a table with connector settings is similar to a source. The difference is that a table object with connector settings persists raw streaming data in the source, while a source object does not. To use this materialization, add `{{ config(materialized='table_with_connector') }}` to your model SQL files. You need to provide your create table with connector statement as a whole in this model (see [Example model files](https://docs.risingwave.com/docs/dev/use-dbt/#example-model-files) for details). Because dbt tables have their own semantics, RisingWave use `table_with_connector` to distinguish itself from a dbt table.| -|`sink`| Yes| Creates a [sink](https://docs.risingwave.com/docs/dev/sql-create-sink/). To use this materialization, add `{{ config(materialized='sink') }}` to your SQL files. You need to provide your create sink statement as a whole in this model. See [Example model files](https://docs.risingwave.com/docs/dev/use-dbt/#example-model-files) for details.| +|`source`| Yes| Creates a [source](https://docs.risingwave.com/sql/commands/sql-create-source). To use this materialization, add \{\{ config(materialized='source') \}\} to your model SQL files. You need to provide your create source statement as a whole in this model. See [Example model files](https://docs.risingwave.com/integrations/other/dbt#example-model-files) for details.| +|`table_with_connector`| Yes| Creates a table with connector settings. In RisingWave, a table with connector settings is similar to a source. The difference is that a table object with connector settings persists raw streaming data in the source, while a source object does not. To use this materialization, add `{{ config(materialized='table_with_connector') }}` to your model SQL files. You need to provide your create table with connector statement as a whole in this model (see [Example model files](https://docs.risingwave.com/integrations/other/dbt#example-model-files) for details). Because dbt tables have their own semantics, RisingWave use `table_with_connector` to distinguish itself from a dbt table.| +|`sink`| Yes| Creates a [sink](https://docs.risingwave.com/sql/commands/sql-create-sink). To use this materialization, add `{{ config(materialized='sink') }}` to your SQL files. You need to provide your create sink statement as a whole in this model. See [Example model files](https://docs.risingwave.com/integrations/other/dbt#example-model-files) for details.| ## Resources -- [RisingWave's guide about using dbt for data transformations](https://docs.risingwave.com/docs/dev/use-dbt/) -- [A demo project using dbt to manage Nexmark benchmark queries in RisingWave](https://docs.risingwave.com/docs/dev/use-dbt/) +- [RisingWave's guide about using dbt for data transformations](https://docs.risingwave.com/integrations/other/dbt) +- [A demo project using dbt to manage Nexmark benchmark queries in RisingWave](https://github.com/risingwavelabs/dbt_rw_nexmark) diff --git a/website/docs/docs/core/connect-data-platform/snowflake-setup.md b/website/docs/docs/core/connect-data-platform/snowflake-setup.md index 266840cafae..b692ba5c0d6 100644 --- a/website/docs/docs/core/connect-data-platform/snowflake-setup.md +++ b/website/docs/docs/core/connect-data-platform/snowflake-setup.md @@ -211,7 +211,7 @@ my-snowflake-db: -### SSO Authentication +### SSO authentication To use SSO authentication for Snowflake, omit a `password` and instead supply an `authenticator` config to your target. `authenticator` can be one of 'externalbrowser' or a valid Okta URL. @@ -332,7 +332,7 @@ my-snowflake-db: -### SSO Authentication +### SSO authentication To use SSO authentication for Snowflake, omit a `password` and instead supply an `authenticator` config to your target. `authenticator` can be one of 'externalbrowser' or a valid Okta URL. @@ -421,6 +421,30 @@ my-snowflake-db: Refer to the [Snowflake docs](https://docs.snowflake.com/en/sql-reference/parameters.html#label-allow-id-token) for info on how to enable this feature in your account. +### OAuth authorization + +To learn how to configure OAuth in Snowflake, refer to their [documentation](https://docs.snowflake.com/en/user-guide/oauth-snowflake-overview). Your Snowflake admin needs to generate an [OAuth token](https://community.snowflake.com/s/article/HOW-TO-OAUTH-TOKEN-GENERATION-USING-SNOWFLAKE-CUSTOM-OAUTH) for your configuration to work. + +Provide the OAUTH_REDIRECT_URI in Snowflake:`http://localhost:PORT_NUMBER`. For example, `http://localhost:8080`. + +Once your Snowflake admin has configured OAuth, add the following to your `profiles.yml` file: + +```yaml + +my-snowflake-db: + target: dev + outputs: + dev: + type: snowflake + account: [account id] + + # The following fields are retrieved from the Snowflake configuration + authenticator: oauth + oauth_client_id: [OAuth client id] + oauth_client_secret: [OAuth client secret] + token: [OAuth refresh token] +``` + ## Configurations The "base" configs for Snowflake targets are shown below. Note that you should also specify auth-related configs specific to the authentication method you are using as described above. diff --git a/website/docs/docs/core/connect-data-platform/teradata-setup.md b/website/docs/docs/core/connect-data-platform/teradata-setup.md index 7b964b23b3d..f4ffbe37f35 100644 --- a/website/docs/docs/core/connect-data-platform/teradata-setup.md +++ b/website/docs/docs/core/connect-data-platform/teradata-setup.md @@ -8,7 +8,7 @@ meta: github_repo: 'Teradata/dbt-teradata' pypi_package: 'dbt-teradata' min_core_version: 'v0.21.0' - cloud_support: Not Supported + cloud_support: Supported min_supported_version: 'n/a' slack_channel_name: '#db-teradata' slack_channel_link: 'https://getdbt.slack.com/archives/C027B6BHMT3' @@ -18,6 +18,7 @@ meta: Some core functionality may be limited. If you're interested in contributing, check out the source code in the repository listed in the next section. + import SetUpPages from '/snippets/_setup-pages-intro.md'; @@ -26,17 +27,17 @@ import SetUpPages from '/snippets/_setup-pages-intro.md'; ## Python compatibility -| Plugin version | Python 3.9 | Python 3.10 | Python 3.11 | -| -------------- | ----------- | ----------- | ------------ | -|1.0.0.x | ✅ | ❌ | ❌ -|1.1.x.x | ✅ | ✅ | ❌ -|1.2.x.x | ✅ | ✅ | ❌ -|1.3.x.x | ✅ | ✅ | ❌ -|1.4.x.x | ✅ | ✅ | ✅ -|1.5.x | ✅ | ✅ | ✅ -|1.6.x | ✅ | ✅ | ✅ -|1.7.x | ✅ | ✅ | ✅ -|1.8.x | ✅ | ✅ | ✅ +| Plugin version | Python 3.9 | Python 3.10 | Python 3.11 | Python 3.12 | +|----------------|------------|-------------|-------------|-------------| +| 1.0.0.x | ✅ | ❌ | ❌ | ❌ | +| 1.1.x.x | ✅ | ✅ | ❌ | ❌ | +| 1.2.x.x | ✅ | ✅ | ❌ | ❌ | +| 1.3.x.x | ✅ | ✅ | ❌ | ❌ | +| 1.4.x.x | ✅ | ✅ | ✅ | ❌ | +| 1.5.x | ✅ | ✅ | ✅ | ❌ | +| 1.6.x | ✅ | ✅ | ✅ | ❌ | +| 1.7.x | ✅ | ✅ | ✅ | ❌ | +| 1.8.x | ✅ | ✅ | ✅ | ✅ | ## dbt dependent packages version compatibility @@ -46,6 +47,8 @@ import SetUpPages from '/snippets/_setup-pages-intro.md'; | 1.6.7 | 1.6.7 | 1.1.1 | 1.1.1 | | 1.7.x | 1.7.x | 1.1.1 | 1.1.1 | | 1.8.x | 1.8.x | 1.1.1 | 1.1.1 | +| 1.8.x | 1.8.x | 1.2.0 | 1.2.0 | +| 1.8.x | 1.8.x | 1.3.0 | 1.3.0 | ### Connecting to Teradata diff --git a/website/docs/docs/core/connect-data-platform/trino-setup.md b/website/docs/docs/core/connect-data-platform/trino-setup.md index 4caa56dcb00..06c94d7e7ff 100644 --- a/website/docs/docs/core/connect-data-platform/trino-setup.md +++ b/website/docs/docs/core/connect-data-platform/trino-setup.md @@ -34,7 +34,7 @@ The following profile fields are always required except for `user`, which is als | Field | Example | Description | | --------- | ------- | ----------- | -| `host` | `mycluster.mydomain.com` | The hostname of your cluster.

Don't include the `http://` or `https://` prefix. | +| `host` | `mycluster.mydomain.com`

Format for Starburst Galaxy:
  • `mygalaxyaccountname-myclustername.trino.galaxy.starburst.io`
| The hostname of your cluster.

Don't include the `http://` or `https://` prefix. | | `database` | `my_postgres_catalog` | The name of a catalog in your cluster. | | `schema` | `my_schema` | The name of a schema within your cluster's catalog.

It's _not recommended_ to use schema names that have upper case or mixed case letters. | | `port` | `443` | The port to connect to your cluster. By default, it's 443 for TLS enabled clusters. | diff --git a/website/docs/docs/dbt-cloud-apis/discovery-use-cases-and-examples.md b/website/docs/docs/dbt-cloud-apis/discovery-use-cases-and-examples.md index b99853cd547..e095374343f 100644 --- a/website/docs/docs/dbt-cloud-apis/discovery-use-cases-and-examples.md +++ b/website/docs/docs/dbt-cloud-apis/discovery-use-cases-and-examples.md @@ -25,7 +25,7 @@ For performance use cases, people typically query the historical or latest appli It’s helpful to understand how long it takes to build models (tables) and tests to execute during a dbt run. Longer model build times result in higher infrastructure costs and fresh data arriving later to stakeholders. Analyses like these can be in observability tools or ad-hoc queries, like in a notebook. - +
Example query with code diff --git a/website/docs/docs/dbt-cloud-apis/service-tokens.md b/website/docs/docs/dbt-cloud-apis/service-tokens.md index a077b230c28..d9ae52dbc2d 100644 --- a/website/docs/docs/dbt-cloud-apis/service-tokens.md +++ b/website/docs/docs/dbt-cloud-apis/service-tokens.md @@ -25,7 +25,7 @@ You can assign as many permission sets as needed to one token. For more on permi You can generate service tokens if you have a Developer [license](/docs/cloud/manage-access/seats-and-users) and account admin [permissions](/docs/cloud/manage-access/about-user-access#permission-sets). To create a service token in dbt Cloud, follow these steps: -1. Open the **Account Settings** page by clicking the gear icon on the right-hand side. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. 2. On the left sidebar, click on **Service Tokens**. 3. Click the **+ New Token** button to generate a new token. 4. Once the token is generated, you won't be able to view this token again so make sure to save it somewhere safe. diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md index 9178d1e6592..d9ce3bf4fd1 100644 --- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md +++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md @@ -519,7 +519,7 @@ select * from {{ semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'], group_by=[Dimension('metric_time')], limit=10, - order_by=[-'order_gross_profit']) + order_by=['-order_gross_profit']) }} ``` diff --git a/website/docs/docs/dbt-cloud-apis/user-tokens.md b/website/docs/docs/dbt-cloud-apis/user-tokens.md index 02a81d80139..b7bf4fdce28 100644 --- a/website/docs/docs/dbt-cloud-apis/user-tokens.md +++ b/website/docs/docs/dbt-cloud-apis/user-tokens.md @@ -8,7 +8,7 @@ pagination_next: "docs/dbt-cloud-apis/service-tokens" :::Warning -User API tokens have been deprecated and will no longer work. [Migrate](#migrate-from-user-api-keys-to-personal-access-tokens) to personal access tokens to resume services. +User API tokens have been deprecated and will no longer work. [Migrate](#migrate-deprecated-user-api-keys-to-personal-access-tokens) to personal access tokens to resume services. ::: diff --git a/website/docs/docs/dbt-cloud-environments.md b/website/docs/docs/dbt-cloud-environments.md index 6efbd0e36f0..3aa54b4aaed 100644 --- a/website/docs/docs/dbt-cloud-environments.md +++ b/website/docs/docs/dbt-cloud-environments.md @@ -40,7 +40,7 @@ To create a new dbt Cloud development environment: To use the dbt Cloud IDE or dbt Cloud CLI, each developer will need to set up [personal development credentials](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud#get-started-with-the-cloud-ide) to your warehouse connection in their **Profile Settings**. This allows you to set separate target information and maintain individual credentials to connect to your warehouse. - + ## Deployment environment diff --git a/website/docs/docs/dbt-versions/versionless-cloud.md b/website/docs/docs/dbt-versions/cloud-release-tracks.md similarity index 51% rename from website/docs/docs/dbt-versions/versionless-cloud.md rename to website/docs/docs/dbt-versions/cloud-release-tracks.md index 4bb843898c2..290078da572 100644 --- a/website/docs/docs/dbt-versions/versionless-cloud.md +++ b/website/docs/docs/dbt-versions/cloud-release-tracks.md @@ -1,18 +1,61 @@ --- -title: "Upgrade to \"Versionless\" in dbt Cloud" -sidebar_label: "Upgrade to \"Versionless\" " -description: "Learn how to go versionless in dbt Cloud. You never have to perform an upgrade again. Plus, you'll be able to access new features and enhancements as soon as they become available. " +title: "Release tracks in dbt Cloud" +sidebar_label: "dbt Cloud Release Tracks" +description: "Learn how to get automatic upgrades to dbt in dbt Cloud. Access new features and enhancements as soon as they become available." --- -Since May 2024, new capabilities in dbt are delivered continuously to dbt Cloud. We call this "versionless dbt," because your projects and environments are upgraded automatically. +Since May 2024, new capabilities in the dbt framework are delivered continuously to dbt Cloud. Your projects and environments are upgraded automatically on a cadence that you choose, depending on your dbt Cloud plan. + +Previously, customers would pin to a minor version of dbt Core, and receive only patch updates during that specific version's active support period. Release tracks ensure that your project stays up-to-date with the modern capabilities of dbt Cloud and recent versions of dbt Core. This will require you to make one final update to your current jobs and environments. When that's done, you'll never have to think about managing, coordinating, or upgrading dbt versions again. -By moving your environments and jobs to "Versionless," you can get all the functionality in the latest features before they're in dbt Core — and more! — along with access to the new features and fixes as soon as they’re released. +By moving your environments and jobs to release tracks you can get all the functionality in dbt Cloud as soon as it's ready. On the "Latest" release track, this includes access to features _before_ they're available in final releases of dbt Core OSS. + +## Which release tracks are available? + +- **"Latest"** (available to all plans, formerly called "Versionless"): Provides a continuous release of the latest functionality in dbt Cloud. Includes early access to new features of the dbt framework before they're available in open source releases of dbt Core. +- **"Compatible"** (available to Team + Enterprise): Provides a monthly release aligned with the most recent open source versions of dbt Core and adapters, plus functionality exclusively available in dbt Cloud. +- **"Extended"** (available to Enterprise): Provides a delayed release of the previous month's "Compatible" release. + +The first "Compatible" release will be in December 2024, after the final release of dbt Core v1.9.0. For December 2024 only, the "Extended" release is the same as "Compatible." Starting in January 2025, "Extended" will be one month behind "Compatible." + +To configure an environment in the [dbt Cloud Admin API](/docs/dbt-cloud-apis/admin-cloud-api) or [Terraform](https://registry.terraform.io/providers/dbt-labs/dbtcloud/latest) to use a release track, set `dbt_version` to the release track name: +- `latest` (formerly called `versionless`; the old name is still supported) +- `compatible` (available to Team + Enterprise) +- `extended` (available to Enterprise) + +## Which release track should I choose? + +Choose the "Latest" release track to continuously receive new features, fixes, performance improvements — latest & greatest dbt. This is the default for all customers on dbt Cloud. + +Choose the "Compatible" and "Extended" release tracks if you need a less-frequent release cadence, the ability to test new dbt releases before they go live in production, and/or ongoing compatibility with the latest open source releases of dbt Core. -## Tips for upgrading {#upgrade-tips} +### Common architectures -If you regularly develop your dbt project in dbt Cloud and this is your first time trying “Versionless,” dbt Labs recommends that you try upgrading your project in a development environment. [Override your dbt version in development](/docs/dbt-versions/upgrade-dbt-version-in-cloud#override-dbt-version). Then, launch the IDE or Cloud CLI and do your development work as usual. Everything should work as you expect. +**Default** - majority of customers on all plans +- Prioritize immediate access to fixes and features +- Leave all environments on the "Latest" release track (default configuration) + +**Hybrid** - Team, Enterprise +- Prioritize ongoing compatibility between dbt Cloud and dbt Core for development & deployment using both products in the same dbt projects +- Configure all environments to use the "Compatible" release track +- Understand that new features will not be available until they are first released in dbt Core OSS (several months after the "Latest" release track) + +**Cautious** - Enterprise, Business Critical +- Prioritize "bake in" time for new features & fixes +- Configure development & test environments to use the "Compatible" release track +- Configure pre-production & production environments to use the "Extended" release track +- Understand that new features will not be available until they are first released in dbt Core OSS + Compatible track + +**Virtual Private dbt or Single Tenant** +- Changes to all release tracks roll out as part of dbt Cloud instance upgrades once per week + +## Upgrading from older versions + +### How to upgrade {#upgrade-tips} + +If you regularly develop your dbt project in dbt Cloud, and you're still running on a legacy version of dbt Core, dbt Labs recommends that you try upgrading your project in a development environment. [Override your dbt version in development](/docs/dbt-versions/upgrade-dbt-version-in-cloud#override-dbt-version). Then, launch the IDE or Cloud CLI and do your development work as usual. Everything should work as you expect. If you do see something unexpected or surprising, revert back to the previous version and record the differences you observed. [Contact dbt Cloud support](/docs/dbt-support#dbt-cloud-support) with your findings for a more detailed investigation. @@ -20,25 +63,23 @@ Next, we recommend that you try upgrading your project’s [deployment environme If your organization has multiple dbt projects, we recommend starting your upgrade with projects that are smaller, newer, or more familiar for your team. That way, if you do encounter any issues, it'll be easier and faster to troubleshoot those before proceeding to upgrade larger or more complex projects. -## Considerations - -The following is our guidance on some important considerations regarding dbt projects as part of the upgrade. +### Considerations -To learn more about how dbt Labs deploys stable dbt upgrades in a safe manner to dbt Cloud, we recommend that you read our blog post [How we're making sure you can confidently go "Versionless" in dbt Cloud](https://docs.getdbt.com/blog/latest-dbt-stability) for details. +To learn more about how dbt Labs deploys stable dbt upgrades in a safe manner to dbt Cloud, we recommend that you read our blog post: [How we're making sure you can confidently switch to the \"Latest\" release track in dbt Cloud](https://docs.getdbt.com/blog/latest-dbt-stability). If you're running dbt version 1.6 or older, please know that your version of dbt Core has reached [end-of-life (EOL)](/docs/dbt-versions/core#eol-version-support) and is no longer supported. We strongly recommend that you update to a newer version as soon as reasonably possible. -dbt Labs has extended the critical support period of dbt Core v1.7 for dbt Cloud Enterprise customers. +dbt Labs has extended the critical support period of dbt Core v1.7 for dbt Cloud Enterprise customers to January 31, 2024. At that point, we will be asking all customers to select a Release Track for receiving ongoing updates to dbt in dbt Cloud. If you're running dbt version v1.6 or older, please know that your version of dbt Core has reached [end-of-life (EOL)](/docs/dbt-versions/core#eol-version-support) and is no longer supported. We strongly recommend that you update to a newer version as soon as reasonably possible. -dbt Labs has extended the "Critical Support" period of dbt Core v1.7 for dbt Cloud Enterprise customers while we work through the migration with those customers to automatic upgrades. In the meantime, this means that v1.7 will continue to be accessible in dbt Cloud for Enteprise customers, jobs and environments on v1.7 for those customers will not be automatically migrated to "Versionless," and dbt Labs will continue to fix critical bugs and security issues. +dbt Labs has extended the "Critical Support" period of dbt Core v1.7 for dbt Cloud Enterprise customers while we work through the migration with those customers to Release Tracks. In the meantime, this means that v1.7 will continue to be accessible in dbt Cloud for Enteprise customers, jobs and environments on v1.7 for those customers will not be automatically migrated to "Latest," and dbt Labs will continue to fix critical bugs and security issues. -dbt Cloud accounts on the Developer and Team plans will be migrated to "Versionless" dbt after November 1, 2024. If you know that your project will not be compatible with the upgrade, for one of the reasons described here, or a different reason in your own testing, you should [contact dbt Cloud support](https://docs.getdbt.com/docs/dbt-support#dbt-cloud-support) to request an extension. +dbt Cloud accounts on the Developer and Team plans will be migrated to the "Latest" release track after November 1, 2024. If you know that your project will not be compatible with the upgrade, for one of the reasons described here, or a different reason in your own testing, you should [contact dbt Cloud support](https://docs.getdbt.com/docs/dbt-support#dbt-cloud-support) to request an extension. -If your account has been migrated to "Versionless," and you are seeing net-new failures in your scheduled dbt jobs, you should also [contact dbt Cloud support](https://docs.getdbt.com/docs/dbt-support#dbt-cloud-support) to request an extension. +If your account has been migrated to the "Latest" release track, and you are seeing net-new failures in your scheduled dbt jobs, you should also [contact dbt Cloud support](https://docs.getdbt.com/docs/dbt-support#dbt-cloud-support) to request an extension. @@ -51,16 +92,17 @@ The legacy dbt Semantic Layer was deprecated in the second half of 2023. We reco If you are upgrading from a very old unsupported version of dbt Core, you may run into one of these edge cases after the upgrade to a newer version: +- [v1.1] Customers on BigQuery should be aware that dbt Cloud sets a default [per-model timeout](/docs/core/connect-data-platform/bigquery-setup#job_execution_timeout_seconds) of 5 minutes. You may override this config in your connection details. Older versions of dbt (including v1.0) did not appropriately respect this timeout configuration. - [v1.3] Customers with non-dbt `.py` files defined within their project directories, such as `models/`. Since v1.3, dbt expects these files be valid [Python models](/docs/build/python-models). The customer needs to move these files out of their `models/` directory, or ignore them via `.dbtignore` - [v1.5] Customers who have `--m` in their job definitions, instead of `-m` or `--models`. This autocompletion (`--m[odels]` for `--models`) has never been officially documented or supported. It was an implicit behavior of argparse (CLI library used in dbt-core v1.0-1.4) that is not supported by `click` (the CLI library used in dbt-core since v1.5+). - [v1.5] Empty invalid `tests` config start raising a validation error](https://docs.getdbt.com/docs/dbt-versions/core-upgrade/upgrading-to-v1.5). Replace empty `tests` config with `tests: []` or remove it altogether. -- [v1.6] Performance optimization to `load_result` means you cannot call it on the same query result multiple times (https://github.com/dbt-labs/dbt-core/pull/7371) +- [v1.6] Performance optimization to `load_result` means you cannot call it on the same query result multiple times. Instead, save it to a local variable once, and reuse that variable (context: [dbt-core#7371](https://github.com/dbt-labs/dbt-core/pull/7371) You should [contact dbt Cloud support](https://docs.getdbt.com/docs/dbt-support#dbt-cloud-support) to request an extension, during which you will need to make those updates. - + For the vast majority of customers, there is no further action needed. @@ -74,9 +116,9 @@ When we talk about _latest version_, we’re referring to the underlying runtime If a new version of a dbt package includes a breaking change (for example, a change to one of the macros in `dbt_utils`), you don’t have to immediately use the new version. In your `packages` configuration (in `dependencies.yml` or `packages.yml`), you can still specify which versions or version ranges of packages you want dbt to install. If you're not already doing so, we strongly recommend [checking `package-lock.yml` into version control](/reference/commands/deps#predictable-package-installs) for predictable package installs in deployment environments and a clear change history whenever you install upgrades. -If you upgrade to “Versionless” and immediately see something that breaks, please [contact support](/docs/dbt-support#dbt-cloud-support) and, in the meantime, downgrade back to v1.7. +If you upgrade to the "Latest" release track, and immediately see something that breaks, please [contact support](/docs/dbt-support#dbt-cloud-support) and, in the meantime, downgrade back to v1.7. -If you’re already on “Versionless” and you observe a breaking change (like something worked yesterday, but today it isn't working, or works in a surprising/different way), please [contact support](/docs/dbt-support#dbt-cloud-support) immediately. Depending on your contracted support agreement, the dbt Labs team will respond within our SLA time and we would seek to roll back the change and/or roll out a fix (just as we would for any other part of dbt Cloud). This is the same whether or not the root cause of the breaking change is in the project code or in the code of a package. +If you’re already on the "Latest" release track, and you observe a breaking change (like something worked yesterday, but today it isn't working, or works in a surprising/different way), please [contact support](/docs/dbt-support#dbt-cloud-support) immediately. Depending on your contracted support agreement, the dbt Labs team will respond within our SLA time and we would seek to roll back the change and/or roll out a fix (just as we would for any other part of dbt Cloud). This is the same whether or not the root cause of the breaking change is in the project code or in the code of a package. If the package you’ve installed relies on _undocumented_ functionality of dbt, it doesn't have the same guarantees as functionality that we’ve documented and tested. However, we will still do our best to avoid breaking them. diff --git a/website/docs/docs/dbt-versions/compatible-track-changelog.md b/website/docs/docs/dbt-versions/compatible-track-changelog.md new file mode 100644 index 00000000000..8f31775e3f1 --- /dev/null +++ b/website/docs/docs/dbt-versions/compatible-track-changelog.md @@ -0,0 +1,27 @@ +--- +title: "dbt Cloud Compatible Track - Changelog" +sidebar_label: "Compatible Track Changelog" +description: "The Compatible release track updates once per month, and it includes up-to-date open source versions as of the monthly release." +--- + +:::info Coming soon + +The "Compatible" and "Extended" release tracks will be available in Preview to eligible dbt Cloud accounts in December 2024. + +::: + +Select the "Compatible" and "Extended" release tracks if you need a less-frequent release cadence, the ability to test new dbt releases before they go live in production, and/or ongoing compatibility with the latest open source releases of dbt Core. + +Each monthly "Compatible" release includes functionality matching up-to-date open source versions of dbt Core and adapters at the time of release. + +Starting in January 2025, each monthly "Extended" release will match the previous month's "Compatible" release. + +For more information, see [release tracks](/docs/dbt-versions/cloud-release-tracks). + +## December 2024 + +Planned release: December 11-13 + +This release will include functionality from `dbt-core==1.9.0` and the most recent versions of all adapters supported in dbt Cloud. After the Compatible release is cut, we will update with: +- exact versions of open source dbt packages +- changelog notes concerning functionality specific to dbt Cloud diff --git a/website/docs/docs/dbt-versions/core-upgrade/06-upgrading-to-v1.9.md b/website/docs/docs/dbt-versions/core-upgrade/06-upgrading-to-v1.9.md index aaa85e4ecef..9a4712af528 100644 --- a/website/docs/docs/dbt-versions/core-upgrade/06-upgrading-to-v1.9.md +++ b/website/docs/docs/dbt-versions/core-upgrade/06-upgrading-to-v1.9.md @@ -1,5 +1,5 @@ --- -title: "Upgrading to v1.9 (beta)" +title: "Upgrading to v1.9" id: upgrading-to-v1.9 description: New features and changes in dbt Core v1.9 displayed_sidebar: "docs" @@ -9,14 +9,15 @@ displayed_sidebar: "docs" - [dbt Core 1.9 changelog](https://github.com/dbt-labs/dbt-core/blob/1.9.latest/CHANGELOG.md) - [dbt Core CLI Installation guide](/docs/core/installation-overview) -- [Cloud upgrade guide](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) +- [Cloud upgrade guide](/docs/dbt-versions/upgrade-dbt-version-in-cloud#release-tracks) ## What to know before upgrading dbt Labs is committed to providing backward compatibility for all versions 1.x. Any behavior changes will be accompanied by a [behavior change flag](/reference/global-configs/behavior-changes#behavior-change-flags) to provide a migration window for existing projects. If you encounter an error upon upgrading, please let us know by [opening an issue](https://github.com/dbt-labs/dbt-core/issues/new). -dbt Cloud is now [versionless](/docs/dbt-versions/versionless-cloud). If you have selected "Versionless" in dbt Cloud, you already have access to all the features, fixes, and other functionality that is included in dbt Core v1.9. -For users of dbt Core, since v1.8 we recommend explicitly installing both `dbt-core` and `dbt-`. This may become required for a future version of dbt. For example: +Starting in 2024, dbt Cloud provides the functionality from new versions of dbt Core via [release tracks](/docs/dbt-versions/cloud-release-tracks) with automatic upgrades. If you have selected the "Latest" release track in dbt Cloud, you already have access to all the features, fixes, and other functionality that is included in dbt Core v1.9! If you have selected the "Compatible" release track, you will have access in the next monthly "Compatible" release after the dbt Core v1.9 final release. + +For users of dbt Core, since v1.8, we recommend explicitly installing both `dbt-core` and `dbt-`. This may become required for a future version of dbt. For example: ```sql python3 -m pip install dbt-core dbt-snowflake @@ -29,7 +30,8 @@ Features and functionality new in dbt v1.9. ### Microbatch `incremental_strategy` :::info -While microbatch is in "beta", this functionality is still gated behind an env var, which will change to a behavior flag when 1.9 is GA. To use microbatch, set `DBT_EXPERIMENTAL_MICROBATCH` to `true` wherever you're running dbt Core. + +If you use a custom microbatch macro, set the [`require_batched_execution_for_custom_microbatch_strategy`](/reference/global-configs/behavior-changes#custom-microbatch-strategy) behavior flag in your `dbt_project.yml` to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the microbatch strategy. ::: Incremental models are, and have always been, a *performance optimization* — for datasets that are too large to be dropped and recreated from scratch every time you do a `dbt run`. Learn more about [incremental models](/docs/build/incremental-models-overview). @@ -47,12 +49,16 @@ Starting in Core 1.9, you can use the new [microbatch strategy](/docs/build/incr - Simplified query design: Write your model query for a single batch of data. dbt will use your `event_time`, `lookback`, and `batch_size` configurations to automatically generate the necessary filters for you, making the process more streamlined and reducing the need for you to manage these details. - Independent batch processing: dbt automatically breaks down the data to load into smaller batches based on the specified `batch_size` and processes each batch independently, improving efficiency and reducing the risk of query timeouts. If some of your batches fail, you can use `dbt retry` to load only the failed batches. - Targeted reprocessing: To load a *specific* batch or batches, you can use the CLI arguments `--event-time-start` and `--event-time-end`. +- [Automatic parallel batch execution](/docs/build/incremental-microbatch#parallel-batch-execution): Process multiple batches at the same time, instead of one after the other (sequentially) for faster processing of your microbatch models. dbt intelligently auto-detects if your batches can run in parallel, while also allowing you to manually override parallel execution with the [`concurrent_batches` config](/reference/resource-properties/concurrent_batches). + Currently microbatch is supported on these adapters with more to come: * postgres + * redshift * snowflake * bigquery * spark + * databricks ### Snapshots improvements @@ -63,9 +69,13 @@ Beginning in dbt Core 1.9, we've streamlined snapshot configuration and added a - `target_schema` is now optional for snapshots: When omitted, snapshots will use the schema defined for the current environment. - Standard `schema` and `database` configs supported: Snapshots will now be consistent with other dbt resource types. You can specify where environment-aware snapshots should be stored. - Warning for incorrect `updated_at` data type: To ensure data integrity, you'll see a warning if the `updated_at` field specified in the snapshot configuration is not the proper data type or timestamp. +- Set a custom current indicator for the value of `dbt_valid_to`: Use the [`dbt_valid_to_current` config](/reference/resource-configs/dbt_valid_to_current) to set a custom indicator for the value of `dbt_valid_to` in current snapshot records (like a future date). By default, this value is `NULL`. When configured, dbt will use the specified value instead of `NULL` for `dbt_valid_to` for current records in the snapshot table. +- Use the [`hard_deletes`](/reference/resource-configs/hard-deletes) configuration to get more control on how to handle deleted rows from the source. Supported methods are `ignore` (default), `invalidate` (replaces legacy `invalidate_hard_deletes=true`), and `new_record`. Setting `hard_deletes='new_record'` allows you to track hard deletes by adding a new record when row becomes "deleted" in source. Read more about [Snapshots meta fields](/docs/build/snapshots#snapshot-meta-fields). +To learn how to safely migrate existing snapshots, refer to [Snapshot configuration migration](/reference/snapshot-configs#snapshot-configuration-migration) for more information. + ### `state:modified` improvements We’ve made improvements to `state:modified` behaviors to help reduce the risk of false positives and negatives. Read more about [the `state:modified` behavior flag](#managing-changes-to-legacy-behaviors) that unlocks this improvement: @@ -82,6 +92,8 @@ You can read more about each of these behavior changes in the following links: - (Introduced, disabled by default) [`skip_nodes_if_on_run_start_fails` project config flag](/reference/global-configs/behavior-changes#behavior-change-flags). If the flag is set and **any** `on-run-start` hook fails, mark all selected nodes as skipped. - `on-run-start/end` hooks are **always** run, regardless of whether they passed or failed last time. - (Introduced, disabled by default) [[Redshift] `restrict_direct_pg_catalog_access`](/reference/global-configs/behavior-changes#redshift-restrict_direct_pg_catalog_access). If the flag is set the adapter will use the Redshift API (through the Python client) if available, or query Redshift's `information_schema` tables instead of using `pg_` tables. +- (Introduced, disabled by default) [`require_nested_cumulative_type_params`](/reference/global-configs/behavior-changes#cumulative-metrics). If the flag is set to `True`, users will receive an error instead of a warning if they're not proprly formatting cumulative metrics using the new [`cumulative_type_params`](/docs/build/cumulative#parameters) nesting. +- (Introduced, disabled by default) [`require_batched_execution_for_custom_microbatch_strategy`](/reference/global-configs/behavior-changes#custom-microbatch-strategy). Set to `True` if you use a custom microbatch macro to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the microbatch strategy. ## Adapter specific features and functionalities @@ -91,7 +103,7 @@ You can read more about each of these behavior changes in the following links: ### Snowflake -- Iceberg Table Format support will be available on three out of the box materializations: table, incremental, dynamic tables. +- Iceberg Table Format support will be available on three out-of-the-box materializations: table, incremental, dynamic tables. ### Bigquery @@ -106,7 +118,7 @@ You can read more about each of these behavior changes in the following links: We also made some quality-of-life improvements in Core 1.9, enabling you to: -- Maintain data quality now that dbt returns an an error (versioned models) or warning (unversioned models) when someone [removes a contracted model by deleting, renaming, or disabling](/docs/collaborate/govern/model-contracts#how-are-breaking-changes-handled) it. -- Document [singular data tests](/docs/build/data-tests#singular-data-tests). +- Maintain data quality now that dbt returns an error (versioned models) or warning (unversioned models) when someone [removes a contracted model by deleting, renaming, or disabling](/docs/collaborate/govern/model-contracts#how-are-breaking-changes-handled) it. +- Document [data tests](/reference/resource-properties/description). - Use `ref` and `source` in [foreign key constraints](/reference/resource-properties/constraints). - Use `dbt test` with the `--resource-type` / `--exclude-resource-type` flag, making it possible to include or exclude data tests (`test`) or unit tests (`unit_test`). diff --git a/website/docs/docs/dbt-versions/core-upgrade/07-upgrading-to-v1.8.md b/website/docs/docs/dbt-versions/core-upgrade/07-upgrading-to-v1.8.md index 9163047e7e0..e9e45a69153 100644 --- a/website/docs/docs/dbt-versions/core-upgrade/07-upgrading-to-v1.8.md +++ b/website/docs/docs/dbt-versions/core-upgrade/07-upgrading-to-v1.8.md @@ -15,13 +15,9 @@ displayed_sidebar: "docs" dbt Labs is committed to providing backward compatibility for all versions 1.x, except for any changes explicitly mentioned on this page. If you encounter an error upon upgrading, please let us know by [opening an issue](https://github.com/dbt-labs/dbt-core/issues/new). -## Versionless +## Release tracks -dbt Cloud is going "versionless." This means you'll automatically get early access to new features and functionality before they're available in final releases of dbt Core. - -Select [**Versionless**](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) in your development, staging, and production [environments](/docs/deploy/deploy-environments) to access to everything in dbt Core v1.8+ and more. - -To upgrade an environment in the [dbt Cloud Admin API](/docs/dbt-cloud-apis/admin-cloud-api) or [Terraform](https://registry.terraform.io/providers/dbt-labs/dbtcloud/latest), set `dbt_version` to the string `versionless`. +Starting in 2024, dbt Cloud provides the functionality from new versions of dbt Core via [release tracks](/docs/dbt-versions/cloud-release-tracks) with automatic upgrades. Select a release track in your development, staging, and production [environments](/docs/deploy/deploy-environments) to access everything in dbt Core v1.8+ and more. To upgrade an environment in the [dbt Cloud Admin API](/docs/dbt-cloud-apis/admin-cloud-api) or [Terraform](https://registry.terraform.io/providers/dbt-labs/dbtcloud/latest), set `dbt_version` to the string `latest`. ## New and changed features and functionality diff --git a/website/docs/docs/dbt-versions/core-versions.md b/website/docs/docs/dbt-versions/core-versions.md index 4a490f96bd5..2f3cec44191 100644 --- a/website/docs/docs/dbt-versions/core-versions.md +++ b/website/docs/docs/dbt-versions/core-versions.md @@ -8,11 +8,11 @@ pagination_prev: null dbt Core releases follow [semantic versioning](https://semver.org/) guidelines. For more on how we use semantic versions, see [How dbt Core uses semantic versioning](#how-dbt-core-uses-semantic-versioning). -:::tip Go versionless and stay up to date, always +:::tip Release Tracks keep you up to date, always _Did you know that you can always be working with the latest features and functionality?_ -With dbt Cloud, you can get early access to new functionality before it becomes available in dbt Core and without the need of managing your own version upgrades. Refer to the [Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) setting for details. +With dbt Cloud, you can get early access to new functionality before it becomes available in dbt Core and without the need of managing your own version upgrades. Refer to the ["Latest" Release Track](/docs/dbt-versions/cloud-release-tracks) setting for details. ::: diff --git a/website/docs/docs/dbt-versions/experimental-features.md b/website/docs/docs/dbt-versions/experimental-features.md index a621bd4ac44..cc5bf3ff748 100644 --- a/website/docs/docs/dbt-versions/experimental-features.md +++ b/website/docs/docs/dbt-versions/experimental-features.md @@ -18,7 +18,8 @@ You can access experimental features to preview beta features that haven’t yet To enable or disable experimental features: -1. Navigate to **Profile settings** by clicking the gear icon in the top right. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings** +2. Go to **Personal profile** under the **Your profile** header. 2. Find Experimental features at the bottom of Your Profile page. 3. Click **Beta** to toggle the features on or off as shown in the following image. ![Experimental features](/img/docs/dbt-versions/experimental-feats.png) diff --git a/website/docs/docs/dbt-versions/product-lifecycles.md b/website/docs/docs/dbt-versions/product-lifecycles.md index e8711c825c4..01a8628d3ca 100644 --- a/website/docs/docs/dbt-versions/product-lifecycles.md +++ b/website/docs/docs/dbt-versions/product-lifecycles.md @@ -17,7 +17,7 @@ dbt Cloud features all fall into one of the following categories: - **Beta:** Beta features are still in development and are only available to select customers. To join a beta, there might be a signup form or dbt Labs may contact specific customers about testing. Some features can be activated by enabling [experimental features](/docs/dbt-versions/experimental-features) in your account. Beta features are incomplete and might not be entirely stable; they should be used at the customer’s risk, as breaking changes could occur. Beta features might not be fully documented, technical support is limited, and service level objectives (SLOs) might not be provided. Download the [Beta Features Terms and Conditions](/assets/beta-tc.pdf) for more details. - **Preview:** Preview features are stable and considered functionally ready for production deployments. Some planned additions and modifications to feature behaviors could occur before they become generally available. New functionality that is not backward compatible could also be introduced. Preview features include documentation, technical support, and service level objectives (SLOs). Features in preview are provided at no extra cost, although they might become paid features when they become generally available. -- **Generally available (GA):** Generally available features provide stable features introduced to all qualified dbt Cloud accounts. Service level agreements (SLAs) apply to GA features, including documentation and technical support. Certain GA feature availability is determined by the dbt version of the environment. To always receive the latest GA features, ensure your dbt Cloud [environments](/docs/dbt-cloud-environments) are set to ["Versionless"](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless). +- **Generally available (GA):** Generally available features provide stable features introduced to all qualified dbt Cloud accounts. Service level agreements (SLAs) apply to GA features, including documentation and technical support. Certain GA feature availability is determined by the dbt version of the environment. To always receive the latest GA features, ensure your dbt Cloud [environments](/docs/dbt-cloud-environments) are on a supported [Release Track](/docs/dbt-versions/cloud-release-tracks). - **Deprecated:** Features in this state are no longer being developed or enhanced by dbt Labs. They will continue functioning as-is, and their documentation will persist until their removal date. However, they are no longer subject to technical support. - **Removed:** Removed features are no longer available on the platform in any capacity. diff --git a/website/docs/docs/dbt-versions/release-notes.md b/website/docs/docs/dbt-versions/release-notes.md index d96d5207bd3..ad8e2d3a9a9 100644 --- a/website/docs/docs/dbt-versions/release-notes.md +++ b/website/docs/docs/dbt-versions/release-notes.md @@ -18,7 +18,8 @@ Release notes are grouped by month for both multi-tenant and virtual private clo \* The official release date for this new format of release notes is May 15th, 2024. Historical release notes for prior dates may not reflect all available features released earlier this year or their tenancy availability. -## November 2024 +## December 2024 + - **New**: Exports now support [tags](/reference/resource-configs/tags) in dbt. Tags allow you to categorize your resources and filter them. You can add tags to your [exports](/docs/build/saved-queries#configure-exports) in the `semantic_model.yml` file or `dbt_project.yml` file. For example: ```yml @@ -27,14 +28,31 @@ Release notes are grouped by month for both multi-tenant and virtual private clo tags: ['export_tag'] ... ``` +- **New**: [Model notifications](/docs/deploy/model-notifications) are now generally available in dbt Cloud. These notifications alert model owners through email about any issues encountered by models and tests as soon as they occur while running a job. +- **New**: You can now use your [Azure OpenAI key](/docs/cloud/account-integrations?ai-integration=azure#ai-integrations) (available in beta) to use dbt Cloud features like [dbt Copilot](/docs/cloud/dbt-copilot) and [Ask dbt](/docs/cloud-integrations/snowflake-native-app) . Additionally, you can use your own [OpenAI API key](/docs/cloud/account-integrations?ai-integration=openai#ai-integrations) or use [dbt Labs-managed OpenAI](/docs/cloud/account-integrations?ai-integration=dbtlabs#ai-integrations) key. Refer to [AI integrations](/docs/cloud/account-integrations#ai-integrations) for more information. +- **New**: The [`hard_deletes`](/reference/resource-configs/hard-deletes) config gives you more control on how to handle deleted rows from the source. Supported options are `ignore` (default), `invalidate` (replaces the legacy `invalidate_hard_deletes=true`), and `new_record`. Note that `new_record` will create a new metadata column in the snapshot table. + + +## November 2024 + +- **Enhancement**: Trust signal icons in dbt Explorer are now available for Exposures, providing a quick view of data health while browsing resources. To view trust signal icons, go to dbt Explorer and click **Exposures** under the **Resource** tab. Refer to [Trust signal for resources](/docs/collaborate/explore-projects#trust-signals-for-resources) for more info. +- **Bug**: Identified and fixed an error with Semantic Layer queries that take longer than 10 minutes to complete. +- **Fix**: Job environment variable overrides in credentials are now respected for Exports. Previously, they were ignored. +- **Behavior change**: If you use a custom microbatch macro, set a [`require_batched_execution_for_custom_microbatch_strategy` behavior flag](/reference/global-configs/behavior-changes#custom-microbatch-strategy) in your `dbt_project.yml` to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the [microbatch strategy](/docs/build/incremental-microbatch#how-microbatch-compares-to-other-incremental-strategies). +- **Enhancement**: For users that have Advanced CI's [compare changes](/docs/deploy/advanced-ci#compare-changes) feature enabled, you can optimize performance when running comparisons by using custom dbt syntax to customize deferral usage, exclude specific large models (or groups of models with tags), and more. Refer to [Compare changes custom commands](/docs/deploy/job-commands#compare-changes-custom-commands) for examples of how to customize the comparison command. +- **New**: SQL linting in CI jobs is now generally available in dbt Cloud. You can enable SQL linting in your CI jobs, using [SQLFluff](https://sqlfluff.com/), to automatically lint all SQL files in your project as a run step before your CI job builds. SQLFluff linting is available on [dbt Cloud release tracks](/docs/dbt-versions/cloud-release-tracks) and to dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) accounts. Refer to [SQL linting](/docs/deploy/continuous-integration#sql-linting) for more information. +- **New**: Use the [`dbt_valid_to_current`](/reference/resource-configs/dbt_valid_to_current) config to set a custom indicator for the value of `dbt_valid_to` in current snapshot records (like a future date). By default, this value is `NULL`. When configured, dbt will use the specified value instead of `NULL` for `dbt_valid_to` for current records in the snapshot table. This feature is available in [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) (formerly called `Versionless`) and dbt Core v1.9 and later. +- **New**: Use the [`event_time`](/reference/resource-configs/event-time) configuration to specify "at what time did the row occur." This configuration is required for [Incremental microbatch](/docs/build/incremental-microbatch) and can be added to ensure you're comparing overlapping times in [Advanced CI's compare changes](/docs/deploy/advanced-ci). Available in [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) (formerly called `Versionless`) and dbt Core v1.9 and higher. - **Fix**: This update improves [dbt Semantic Layer Tableau integration](/docs/cloud-integrations/semantic-layer/tableau) making query parsing more reliable. Some key fixes include: - Error messages for unsupported joins between saved queries and ALL tables. - Improved handling of queries when multiple tables are selected in a data source. - Fixed a bug when an IN filter contained a lot of values. - Better error messaging for queries that can't be parsed correctly. +- **Enhancement**: The dbt Semantic Layer supports creating new credentials for users who don't have permissions to create service tokens. In the **Credentials & service tokens** side panel, the **+Add Service Token** option is unavailable for those users who don't have permission. Instead, the side panel displays a message indicating that the user doesn't have permission to create a service token and should contact their administration. Refer to [Set up dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) for more details. ## October 2024 + Documentation for new features and functionality announced at Coalesce 2024: @@ -42,7 +60,7 @@ Release notes are grouped by month for both multi-tenant and virtual private clo - Iceberg table support for [Snowflake](https://docs.getdbt.com/reference/resource-configs/snowflake-configs#iceberg-table-format) - [Athena](https://docs.getdbt.com/reference/resource-configs/athena-configs) and [Teradata](https://docs.getdbt.com/reference/resource-configs/teradata-configs) adapter support in dbt Cloud - dbt Cloud now hosted on [Azure](https://docs.getdbt.com/docs/cloud/about-cloud/access-regions-ip-addresses) - - Get comfortable with [Versionless dbt Cloud](https://docs.getdbt.com/docs/dbt-versions/versionless-cloud) + - Get comfortable with [dbt Cloud Release Tracks](https://docs.getdbt.com/docs/dbt-versions/cloud-release-tracks) that keep your project up-to-date, automatically — on a cadence appropriate for your team - Scalable [microbatch incremental models](https://docs.getdbt.com/docs/build/incremental-microbatch) - Advanced CI [features](https://docs.getdbt.com/docs/deploy/advanced-ci) - [Linting with CI jobs](https://docs.getdbt.com/docs/deploy/continuous-integration#sql-linting) @@ -58,7 +76,7 @@ Release notes are grouped by month for both multi-tenant and virtual private clo - [Python SDK](https://docs.getdbt.com/docs/dbt-cloud-apis/sl-python) is now generally available - + - **Behavior change:** [Multi-factor authentication](/docs/cloud/manage-access/mfa) is now enforced on all users who log in with username and password credentials. - **Enhancement**: The dbt Semantic Layer JDBC now allows users to paginate `semantic_layer.metrics()` and `semantic_layer.dimensions()` for metrics and dimensions using `page_size` and `page_number` parameters. Refer to [Paginate metadata calls](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) for more information. - **Enhancement**: The dbt Semantic Layer JDBC now allows you to filter your metrics to include only those that contain a specific substring, using the `search` parameter. If no substring is provided, the query returns all metrics. Refer to [Fetch metrics by substring search](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) for more information. @@ -70,17 +88,17 @@ Release notes are grouped by month for both multi-tenant and virtual private clo - **New**: The dbt Cloud IDE supports signed commits for Git, available for Enterprise plans. You can sign your Git commits when pushing them to the repository to prevent impersonation and enhance security. Supported Git providers are GitHub and GitLab. Refer to [Git commit signing](/docs/cloud/dbt-cloud-ide/git-commit-signing.md) for more information. - **New:** With dbt Mesh, you can now enable bidirectional dependencies across your projects. Previously, dbt enforced dependencies to only go in one direction. dbt checks for cycles across projects and raises errors if any are detected. For details, refer to [Cycle detection](/docs/collaborate/govern/project-dependencies#cycle-detection). There's also the [Intro to dbt Mesh](/best-practices/how-we-mesh/mesh-1-intro) guide to help you learn more best practices. - **New**: The [dbt Semantic Layer Python software development kit](/docs/dbt-cloud-apis/sl-python) is now [generally available](/docs/dbt-versions/product-lifecycles). It provides users with easy access to the dbt Semantic Layer with Python and enables developers to interact with the dbt Semantic Layer APIs to query metrics/dimensions in downstream tools. -- **Enhancement**: You can now add a description to a singular data test in dbt Cloud Versionless. Use the [`description` property](/reference/resource-properties/description) to document [singular data tests](/docs/build/data-tests#singular-data-tests). You can also use [docs block](/docs/build/documentation#using-docs-blocks) to capture your test description. The enhancement will be included in upcoming dbt Core 1.9 release. -- **New**: Introducing the [microbatch incremental model strategy](/docs/build/incremental-microbatch) (beta), available in dbt Cloud Versionless and will soon be supported in dbt Core 1.9. The microbatch strategy allows for efficient, batch-based processing of large time-series datasets for improved performance and resiliency, especially when you're working with data that changes over time (like new records being added daily). To enable this feature in dbt Cloud, set the `DBT_EXPERIMENTAL_MICROBATCH` environment variable to `true` in your project. +- **Enhancement**: You can now add a description to a singular data test. Use the [`description` property](/reference/resource-properties/description) to document [singular data tests](/docs/build/data-tests#singular-data-tests). You can also use [docs block](/docs/build/documentation#using-docs-blocks) to capture your test description. The enhancement is available now in [the "Latest" release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks), and it will be included in dbt Core v1.9. +- **New**: Introducing the [microbatch incremental model strategy](/docs/build/incremental-microbatch) (beta), available now in [dbt Cloud Latest](/docs/dbt-versions/cloud-release-tracks) and will soon be supported in dbt Core v1.9. The microbatch strategy allows for efficient, batch-based processing of large time-series datasets for improved performance and resiliency, especially when you're working with data that changes over time (like new records being added daily). To enable this feature in dbt Cloud, set the `DBT_EXPERIMENTAL_MICROBATCH` environment variable to `true` in your project. - **New**: The dbt Semantic Layer supports custom calendar configurations in MetricFlow, available in [Preview](/docs/dbt-versions/product-lifecycles#dbt-cloud). Custom calendar configurations allow you to query data using non-standard time periods like `fiscal_year` or `retail_month`. Refer to [custom calendar](/docs/build/metricflow-time-spine#custom-calendar) to learn how to define these custom granularities in your MetricFlow timespine YAML configuration. -- **New**: In dbt Cloud Versionless, [Snapshots](/docs/build/snapshots) have been updated to use YAML configuration files instead of SQL snapshot blocks. This new feature simplifies snapshot management and improves performance, and will soon be released in dbt Core 1.9. - - Who does this affect? New user on Versionless can define snapshots using the new YAML specification. Users upgrading to Versionless who use snapshots can keep their existing configuration or can choose to migrate their snapshot definitions to YAML. - - Users on dbt 1.8 and earlier: No action is needed; existing snapshots will continue to work as before. However, we recommend upgrading to Versionless to take advantage of the new snapshot features. +- **New**: In the "Latest" release track in dbt Cloud, [Snapshots](/docs/build/snapshots) have been updated to use YAML configuration files instead of SQL snapshot blocks. This new feature simplifies snapshot management and improves performance, and will soon be released in dbt Core 1.9. + - Who does this affect? Users of the "Latest" release track in dbt Cloud can define snapshots using the new YAML specification. Users upgrading to "Latest" who have existing snapshot definitions can keep their existing configurations, or they can choose to migrate their snapshot definitions to YAML. + - Users on older versions: No action is needed; existing snapshots will continue to work as before. However, we recommend upgrading to the "Latest" release track to take advantage of the new snapshot features. - **Behavior change:** Set [`state_modified_compare_more_unrendered_values`](/reference/global-configs/behavior-changes#source-definitions-for-state) to true to reduce false positives for `state:modified` when configs differ between `dev` and `prod` environments. - **Behavior change:** Set the [`skip_nodes_if_on_run_start_fails`](/reference/global-configs/behavior-changes#failures-in-on-run-start-hooks) flag to `True` to skip all selected resources from running if there is a failure on an `on-run-start` hook. -- **Enhancement**: In dbt Cloud Versionless, snapshots defined in SQL files can now use `config` defined in `schema.yml` YAML files. This update resolves the previous limitation that required snapshot properties to be defined exclusively in `dbt_project.yml` and/or a `config()` block within the SQL file. This will also be released in dbt Core 1.9. -- **New**: In dbt Cloud Versionless, the `snapshot_meta_column_names` config allows for customizing the snapshot metadata columns. This feature allows an organization to align these automatically-generated column names with their conventions, and will be included in the upcoming dbt Core 1.9 release. -- **Enhancement**: dbt Cloud versionless began inferring a model's `primary_key` based on configured data tests and/or constraints within `manifest.json`. The inferred `primary_key` is visible in dbt Explorer and utilized by the dbt Cloud [compare changes](/docs/deploy/run-visibility#compare-tab) feature. This will also be released in dbt Core 1.9. Read about the [order dbt infers columns can be used as primary key of a model](https://github.com/dbt-labs/dbt-core/blob/7940ad5c7858ff11ef100260a372f2f06a86e71f/core/dbt/contracts/graph/nodes.py#L534-L541). +- **Enhancement**: In the "Latest" release track in dbt Cloud, snapshots defined in SQL files can now use `config` defined in `schema.yml` YAML files. This update resolves the previous limitation that required snapshot properties to be defined exclusively in `dbt_project.yml` and/or a `config()` block within the SQL file. This will also be released in dbt Core 1.9. +- **New**: In the "Latest" release track in dbt Cloud, the `snapshot_meta_column_names` config allows for customizing the snapshot metadata columns. This feature allows an organization to align these automatically-generated column names with their conventions, and will be included in the upcoming dbt Core 1.9 release. +- **Enhancement**: the "Latest" release track in dbt Cloud infers a model's `primary_key` based on configured data tests and/or constraints within `manifest.json`. The inferred `primary_key` is visible in dbt Explorer and utilized by the dbt Cloud [compare changes](/docs/deploy/run-visibility#compare-tab) feature. This will also be released in dbt Core 1.9. Read about the [order dbt infers columns can be used as primary key of a model](https://github.com/dbt-labs/dbt-core/blob/7940ad5c7858ff11ef100260a372f2f06a86e71f/core/dbt/contracts/graph/nodes.py#L534-L541). - **New:** dbt Explorer now includes trust signal icons, which is currently available as a [Preview](/docs/dbt-versions/product-lifecycles#dbt-cloud). Trust signals offer a quick, at-a-glance view of data health when browsing your dbt models in Explorer. These icons indicate whether a model is **Healthy**, **Caution**, **Degraded**, or **Unknown**. For accurate health data, ensure the resource is up-to-date and has had a recent job run. Refer to [Trust signals](/docs/collaborate/explore-projects#trust-signals-for-resources) for more information. - **New:** Auto exposures are now available in Preview in dbt Cloud. Auto-exposures helps users understand how their models are used in downstream analytics tools to inform investments and reduce incidents. It imports and auto-generates exposures based on Tableau dashboards, with user-defined curation. To learn more, refer to [Auto exposures](/docs/collaborate/auto-exposures). @@ -90,14 +108,14 @@ Release notes are grouped by month for both multi-tenant and virtual private clo - **Fix**: MetricFlow updated `get_and_expire` to replace the unsupported `GETEX` command with a `GET` and conditional expiration, ensuring compatibility with Azure Redis 6.0. - **Enhancement**: The [dbt Semantic Layer Python SDK](/docs/dbt-cloud-apis/sl-python) now supports `TimeGranularity` custom grain for metrics. This feature allows you to define custom time granularities for metrics, such as `fiscal_year` or `retail_month`, to query data using non-standard time periods. - **New**: Use the dbt Copilot AI engine to generate semantic model for your models, now available in beta. dbt Copilot automatically generates documentation, tests, and now semantic models based on the data in your model, . To learn more, refer to [dbt Copilot](/docs/cloud/dbt-copilot). -- **New**: Use the new recommended syntax for [defining `foreign_key` constraints](/reference/resource-properties/constraints) using `refs`, available in dbt Cloud Versionless. This will soon be released in dbt Core v1.9. This new syntax will capture dependencies and works across different environments. +- **New**: Use the new recommended syntax for [defining `foreign_key` constraints](/reference/resource-properties/constraints) using `refs`, available in the "Latest" release track in dbt Cloud. This will soon be released in dbt Core v1.9. This new syntax will capture dependencies and works across different environments. - **Enhancement**: You can now run [Semantic Layer commands](/docs/build/metricflow-commands) commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud). The supported commands are `dbt sl list`, `dbt sl list metrics`, `dbt sl list dimension-values`, `dbt sl list saved-queries`, `dbt sl query`, `dbt sl list dimensions`, `dbt sl list entities`, and `dbt sl validate`. - **New**: Microsoft Excel, a dbt Semantic Layer integration, is now generally available. The integration allows you to connect to Microsoft Excel to query metrics and collaborate with your team. Available for [Excel Desktop](https://pages.store.office.com/addinsinstallpage.aspx?assetid=WA200007100&rs=en-US&correlationId=4132ecd1-425d-982d-efb4-de94ebc83f26) or [Excel Online](https://pages.store.office.com/addinsinstallpage.aspx?assetid=WA200007100&rs=en-US&correlationid=4132ecd1-425d-982d-efb4-de94ebc83f26&isWac=True). For more information, refer to [Microsoft Excel](/docs/cloud-integrations/semantic-layer/excel). - **New**: [Data health tile](/docs/collaborate/data-tile) is now generally available in dbt Explorer. Data health tiles provide a quick at-a-glance view of your data quality, highlighting potential issues in your data. You can embed these tiles in your dashboards to quickly identify and address data quality issues in your dbt project. - **New**: dbt Explorer's Model query history feature is now in Preview for dbt Cloud Enterprise customers. Model query history allows you to view the count of consumption queries for a model based on the data warehouse's query logs. This feature provides data teams insight, so they can focus their time and infrastructure spend on the worthwhile used data products. To learn more, refer to [Model query history](/docs/collaborate/model-query-history). - **Enhancement**: You can now use [Extended Attributes](/docs/dbt-cloud-environments#extended-attributes) and [Environment Variables](/docs/build/environment-variables) when connecting to the Semantic Layer. If you set a value directly in the Semantic Layer Credentials, it will have a higher priority than Extended Attributes. When using environment variables, the default value for the environment will be used. If you're using exports, job environment variable overrides aren't supported yet, but they will be soon. - **New:** There are two new [environment variable defaults](/docs/build/environment-variables#dbt-cloud-context) — `DBT_CLOUD_ENVIRONMENT_NAME` and `DBT_CLOUD_ENVIRONMENT_TYPE`. -- **New:** The [Amazon Athena warehouse connection](/docs/cloud/connect-data-platform/connect-amazon-athena) is available as a public preview for dbt Cloud accounts that have upgraded to [`versionless`](/docs/dbt-versions/versionless-cloud). +- **New:** The [Amazon Athena warehouse connection](/docs/cloud/connect-data-platform/connect-amazon-athena) is available as a public preview for dbt Cloud accounts that have upgraded to [the "Latest" release track](/docs/dbt-versions/cloud-release-tracks). ## August 2024 @@ -223,15 +241,15 @@ The following features are new or enhanced as part of our [dbt Cloud Launch Show - **New**: The [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) introduces [declarative caching](/docs/use-dbt-semantic-layer/sl-cache), allowing you to cache common queries to speed up performance and reduce query compute costs. Available for dbt Cloud Team or Enterprise accounts. -- +- - The **Versionless** setting is now Generally Available (previously Public Preview). + The **Latest** Release Track is now Generally Available (previously Public Preview). - When the new **Versionless** setting is enabled, you get a versionless experience and always get the latest features and early access to new functionality for your dbt project. dbt Labs will handle upgrades behind-the-scenes, as part of testing and redeploying the dbt Cloud application — just like other dbt Cloud capabilities and other SaaS tools that you're using. No more manual upgrades and no more need for _a second sandbox project_ just to try out new features in development. + On this release track, you get automatic upgrades of dbt, including early access to the latest features, fixes, and performance improvements for your dbt project. dbt Labs will handle upgrades behind-the-scenes, as part of testing and redeploying the dbt Cloud application — just like other dbt Cloud capabilities and other SaaS tools that you're using. No more manual upgrades and no more need for _a second sandbox project_ just to try out new features in development. - To learn more about the new setting, refer to [Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) for details. + To learn more about the new setting, refer to [Release Tracks](/docs/dbt-versions/cloud-release-tracks) for details. - + @@ -247,7 +265,7 @@ The following features are new or enhanced as part of our [dbt Cloud Launch Show -- **Behavior change:** Introduced the `require_explicit_package_overrides_for_builtin_materializations` flag, opt-in and disabled by default. If set to `True`, dbt will only use built-in materializations defined in the root project or within dbt, rather than implementations in packages. This will become the default in May 2024 (dbt Core v1.8 and "Versionless" dbt Cloud). Read [Package override for built-in materialization](/reference/global-configs/behavior-changes#package-override-for-built-in-materialization) for more information. +- **Behavior change:** Introduced the `require_explicit_package_overrides_for_builtin_materializations` flag, opt-in and disabled by default. If set to `True`, dbt will only use built-in materializations defined in the root project or within dbt, rather than implementations in packages. This will become the default in May 2024 (dbt Core v1.8 and dbt Cloud release tracks). Read [Package override for built-in materialization](/reference/global-configs/behavior-changes#package-override-for-built-in-materialization) for more information. **dbt Semantic Layer** - **New**: Use Saved selections to [save your query selections](/docs/cloud-integrations/semantic-layer/gsheets#using-saved-selections) within the [Google Sheets application](/docs/cloud-integrations/semantic-layer/gsheets). They can be made private or public and refresh upon loading. @@ -302,15 +320,15 @@ The following features are new or enhanced as part of our [dbt Cloud Launch Show -- +- _Now available in the dbt version dropdown in dbt Cloud — starting with select customers, rolling out to wider availability through February and March._ - When the new **Versionless** setting is enabled, you always get the latest fixes and early access to new functionality for your dbt project. dbt Labs will handle upgrades behind-the-scenes, as part of testing and redeploying the dbt Cloud application — just like other dbt Cloud capabilities and other SaaS tools that you're using. No more manual upgrades and no more need for _a second sandbox project_ just to try out new features in development. + On this release track, you get automatic upgrades of dbt, including early access to the latest features, fixes, and performance improvements for your dbt project. dbt Labs will handle upgrades behind-the-scenes, as part of testing and redeploying the dbt Cloud application — just like other dbt Cloud capabilities and other SaaS tools that you're using. No more manual upgrades and no more need for _a second sandbox project_ just to try out new features in development. - To learn more about the new setting, refer to [Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) for details. + To learn more about the new setting, refer to [Release Tracks](/docs/dbt-versions/cloud-release-tracks) for details. - + diff --git a/website/docs/docs/dbt-versions/upgrade-dbt-version-in-cloud.md b/website/docs/docs/dbt-versions/upgrade-dbt-version-in-cloud.md index 35758d46afd..52faa9385fa 100644 --- a/website/docs/docs/dbt-versions/upgrade-dbt-version-in-cloud.md +++ b/website/docs/docs/dbt-versions/upgrade-dbt-version-in-cloud.md @@ -7,23 +7,28 @@ In dbt Cloud, both [jobs](/docs/deploy/jobs) and [environments](/docs/dbt-cloud- ## Environments -Navigate to the settings page of an environment, then click **Edit**. Click the **dbt version** dropdown bar and make your selection. You can select a previous release of dbt Core or go [**Versionless**](#versionless) (recommended). Be sure to save your changes before navigating away. +Navigate to the settings page of an environment, then click **Edit**. Click the **dbt version** dropdown bar and make your selection. You can select a [release track](#release-tracks) to receive ongoing updates (recommended), or a legacy version of dbt Core. Be sure to save your changes before navigating away. -### Versionless +### Release Tracks -By choosing to go **Versionless**, you opt for an experience that provides the latest features and early access to new functionality for your dbt project. dbt Labs will handle upgrades for you, as part of testing and redeploying the dbt Cloud SaaS application. Versionless always includes the most recent features before they're in dbt Core, and more. +Starting in 2024, your project will be upgraded automatically on a cadence that you choose -You can upgrade to the **Versionless** experience no matter which version of dbt you currently have selected. As a best practice, dbt Labs recommends that you test the upgrade in development first; use the [Override dbt version](#override-dbt-version) setting to test _your_ project on the latest dbt version before upgrading your deployment environments and the default development environment for all your colleagues. +The **Latest** track ensures you have up-to-date dbt Cloud functionality, and early access to new features of the dbt framework. The **Compatible** and **Extended** tracks are designed for customers who need a less-frequent release cadence, the ability to test new dbt releases before they go live in production, and/or ongoing compatibility with the latest open source releases of dbt Core. -To upgrade an environment in the [dbt Cloud Admin API](/docs/dbt-cloud-apis/admin-cloud-api) or [Terraform](https://registry.terraform.io/providers/dbt-labs/dbtcloud/latest), set `dbt_version` to the string `versionless`. +As a best practice, dbt Labs recommends that you test the upgrade in development first; use the [Override dbt version](#override-dbt-version) setting to test _your_ project on the latest dbt version before upgrading your deployment environments and the default development environment for all your colleagues. + +To upgrade an environment in the [dbt Cloud Admin API](/docs/dbt-cloud-apis/admin-cloud-api) or [Terraform](https://registry.terraform.io/providers/dbt-labs/dbtcloud/latest), set `dbt_version` to the name of your release track: +- `latest` (formerly called `versionless`; the old name is still supported) +- `compatible` (available to Team + Enterprise) +- `extended` (available to Enterprise) ### Override dbt version Configure your project to use a different dbt Core version than what's configured in your [development environment](/docs/dbt-cloud-environments#types-of-environments). This _override_ only affects your user account, no one else's. Use this to safely test new dbt features before upgrading the dbt version for your projects. -1. From the gear menu, select **Profile settings**. +1. Click your account name from the left side panel and select **Account settings**. 1. Choose **Credentials** from the sidebar and select a project. This opens a side panel. 1. In the side panel, click **Edit** and scroll to the **User development settings** section. Choose a version from the **dbt version** dropdown and click **Save**. @@ -41,7 +46,7 @@ Configure your project to use a different dbt Core version than what's configure Each job in dbt Cloud can be configured to inherit parameters from the environment it belongs to. - + The example job seen in the screenshot above belongs to the environment "Prod". It inherits the dbt version of its environment as shown by the **Inherited from ENVIRONMENT_NAME (DBT_VERSION)** selection. You may also manually override the dbt version of a specific job to be any of the current Core releases supported by Cloud by selecting another option from the dropdown. diff --git a/website/docs/docs/deploy/advanced-ci.md b/website/docs/docs/deploy/advanced-ci.md index 8d4d6da8897..9081ce0c16c 100644 --- a/website/docs/docs/deploy/advanced-ci.md +++ b/website/docs/docs/deploy/advanced-ci.md @@ -3,6 +3,7 @@ title: "Advanced CI" id: "advanced-ci" sidebar_label: "Advanced CI" description: "Advanced CI enables developers to compare changes by demonstrating the changes the code produces." +image: /img/docs/dbt-cloud/example-ci-compare-changes-tab.png --- # Advanced CI @@ -20,9 +21,14 @@ dbt Labs plans to provide additional Advanced CI features in the near future. Mo ::: +## Prerequisites +- You have a dbt Cloud Enterprise account. +- You have [Advance CI features](/docs/cloud/account-settings#account-access-to-advanced-features) enabled. +- You use a supported data platform: BigQuery, Databricks, Postgres, or Snowflake. Support for additional data platforms coming soon. + ## Compare changes feature {#compare-changes} -For [CI jobs](/docs/deploy/ci-jobs) that have the **dbt compare** option enabled, dbt Cloud compares the changes between the last applied state of the production environment (defaulting to deferral for lower compute costs) and the latest changes from the pull request, whenever a pull request is opened or new commits are pushed. +For [CI jobs](/docs/deploy/ci-jobs) that have the [**dbt compare** option enabled](/docs/deploy/ci-jobs#set-up-ci-jobs), dbt Cloud compares the changes between the last applied state of the production environment (defaulting to deferral for lower compute costs) and the latest changes from the pull request, whenever a pull request is opened or new commits are pushed. dbt reports the comparison differences in: @@ -31,6 +37,16 @@ dbt reports the comparison differences in: +### Optimizing comparisons + +When an [`event_time`](/reference/resource-configs/event-time) column is specified on your model, compare changes can optimize comparisons by using only the overlapping timeframe (meaning the timeframe exists in both the CI and production environment), helping you avoid incorrect row-count changes and return results faster. + +This is useful in scenarios like: +- **Subset of data in CI** — When CI builds only a [subset of data](/best-practices/best-practice-workflows#limit-the-data-processed-when-in-development) (like the most recent 7 days), compare changes would interpret the excluded data as "deleted rows." Configuring `event_time` allows you to avoid this issue by limiting comparisons to the overlapping timeframe, preventing false alerts about data deletions that are just filtered out in CI. +- **Fresher data in CI than in production** — When your CI job includes fresher data than production (because it has run more recently), compare changes would flag the additional rows as "new" data, even though they’re just fresher data in CI. With `event_time` configured, the comparison only includes the shared timeframe and correctly reflects actual changes in the data. + + + ## About the cached data After [comparing changes](#compare-changes), dbt Cloud stores a cache of no more than 100 records for each modified model for preview purposes. By caching this data, you can view the examples of changed data without rerunning the comparison against the data warehouse every time (optimizing for lower compute costs). To display the changes, dbt Cloud uses a cached version of a sample of the data records. These data records are queried from the database using the connection configuration (such as user, role, service account, and so on) that's set in the CI job's environment. diff --git a/website/docs/docs/deploy/artifacts.md b/website/docs/docs/deploy/artifacts.md index cff36bfafba..cb8d3e85da0 100644 --- a/website/docs/docs/deploy/artifacts.md +++ b/website/docs/docs/deploy/artifacts.md @@ -18,7 +18,11 @@ To view a resource, its metadata, and what commands are needed, refer to [genera The following steps are for legacy dbt Docs only. For the current documentation experience, see [dbt Explorer](/docs/collaborate/explore-projects). -While running any job can produce artifacts, you should only associate one production job with a given project to produce the project's artifacts. You can designate this connection on the **Project details** page. To access this page, click the gear icon in the upper right, select **Account Settings**, select your project, and click **Edit** in the lower right. Under **Artifacts**, select the jobs you want to produce documentation and source freshness artifacts for. +While running any job can produce artifacts, you should only associate one production job with a given project to produce the project's artifacts. You can designate this connection on the **Project details** page. To access this page: + +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. +2. Select your project, and click **Edit** in the lower right. +3. Under **Artifacts**, select the jobs you want to produce documentation and source freshness artifacts for. diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md index 12d880d1543..1128dfd7abc 100644 --- a/website/docs/docs/deploy/ci-jobs.md +++ b/website/docs/docs/deploy/ci-jobs.md @@ -6,20 +6,19 @@ description: "Learn how to create and set up CI checks to test code changes befo You can set up [continuous integration](/docs/deploy/continuous-integration) (CI) jobs to run when someone opens a new pull request (PR) in your dbt Git repository. By running and testing only _modified_ models, dbt Cloud ensures these jobs are as efficient and resource conscientious as possible on your data platform. -## Set up CI jobs {#set-up-ci-jobs} - -dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schema builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch as part of your release process, having a separate environment will allow you to set a [custom branch](/faqs/Environments/custom-branch-settings) and, accordingly, the CI job in that dedicated environment will be triggered only when PRs are made to the specified custom branch. To learn more, refer to [Get started with CI tests](/guides/set-up-ci). - -### Prerequisites +## Prerequisites - You have a dbt Cloud account. - CI features: - For both the [concurrent CI checks](/docs/deploy/continuous-integration#concurrent-ci-checks) and [smart cancellation of stale builds](/docs/deploy/continuous-integration#smart-cancellation) features, your dbt Cloud account must be on the [Team or Enterprise plan](https://www.getdbt.com/pricing/). - - The [SQL linting](/docs/deploy/continuous-integration#sql-linting) feature is currently available in [beta](/docs/dbt-versions/product-lifecycles#dbt-cloud) to a limited group of users and is gradually being rolled out. If you're in the beta, the **Linting** option is available for use. + - [SQL linting](/docs/deploy/continuous-integration#sql-linting) is available on [dbt Cloud release tracks](/docs/dbt-versions/cloud-release-tracks) and to dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) accounts. You should have [SQLFluff configured](/docs/deploy/continuous-integration#to-configure-sqlfluff-linting) in your project. - [Advanced CI](/docs/deploy/advanced-ci) features: - For the [compare changes](/docs/deploy/advanced-ci#compare-changes) feature, your dbt Cloud account must be on the [Enterprise plan](https://www.getdbt.com/pricing/) and have enabled Advanced CI features. Please ask your [dbt Cloud administrator to enable](/docs/cloud/account-settings#account-access-to-advanced-ci-features) this feature for you. After enablement, the **dbt compare** option becomes available in the CI job settings. - Set up a [connection with your Git provider](/docs/cloud/git/git-configuration-in-dbt-cloud). This integration lets dbt Cloud run jobs on your behalf for job triggering. - If you're using a native [GitLab](/docs/cloud/git/connect-gitlab) integration, you need a paid or self-hosted account that includes support for GitLab webhooks and [project access tokens](https://docs.gitlab.com/ee/user/project/settings/project_access_tokens.html). If you're using GitLab Free, merge requests will trigger CI jobs but CI job status updates (success or failure of the job) will not be reported back to GitLab. +## Set up CI jobs {#set-up-ci-jobs} + +dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schema builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch as part of your release process, having a separate environment will allow you to set a [custom branch](/faqs/Environments/custom-branch-settings) and, accordingly, the CI job in that dedicated environment will be triggered only when PRs are made to the specified custom branch. To learn more, refer to [Get started with CI tests](/guides/set-up-ci). To make CI job creation easier, many options on the **CI job** page are set to default values that dbt Labs recommends that you use. If you don't want to use the defaults, you can change them. @@ -36,10 +35,17 @@ To make CI job creation easier, many options on the **CI job** page are set to d 4. Options in the **Execution settings** section: - **Commands** — By default, this includes the `dbt build --select state:modified+` command. This informs dbt Cloud to build only new or changed models and their downstream dependents. Importantly, state comparison can only happen when there is a deferred environment selected to compare state to. Click **Add command** to add more [commands](/docs/deploy/job-commands) that you want to be invoked when this job runs. - - **Linting** — Enable this option for dbt to [lint the SQL files](/docs/deploy/continuous-integration#sql-linting) in your project as the first step in `dbt run`. If this check runs into an error, dbt can either **Stop running on error** or **Continue running on error**. + - **Linting** — Enable this option for dbt to [lint the SQL files](/docs/deploy/continuous-integration#sql-linting) in your project as the first step in `dbt run`. If this check runs into an error, dbt can either **Stop running on error** or **Continue running on error**. - **dbt compare** — Enable this option to compare the last applied state of the production environment (if one exists) with the latest changes from the pull request, and identify what those differences are. To enable record-level comparison and primary key analysis, you must add a [primary key constraint](/reference/resource-properties/constraints) or [uniqueness test](/reference/resource-properties/data-tests#unique). Otherwise, you'll receive a "Primary key missing" error message in dbt Cloud. To review the comparison report, navigate to the [Compare tab](/docs/deploy/run-visibility#compare-tab) in the job run's details. A summary of the report is also available from the pull request in your Git provider (see the [CI report example](#example-ci-report)). + + :::info Optimization tip + When you enable the **dbt compare** checkbox, you can customize the comparison command to optimize your CI job. For example, if you have large models that take a long time to compare, you can exclude them to speed up the process using the [`--exclude` flag](/reference/node-selection/exclude). Refer to [compare changes custom commands](/docs/deploy/job-commands#compare-changes-custom-commands) for more details. + + Additionally, if you set [`event_time`](/reference/resource-configs/event-time) in your models/seeds/snapshots/sources, it allows you to compare matching date ranges between tables by filtering to overlapping date ranges. This is useful for faster CI workflow or custom sampling set ups. + ::: + - **Compare changes against an environment (Deferral)** — By default, it’s set to the **Production** environment if you created one. This option allows dbt Cloud to check the state of the code in the PR against the code running in the deferred environment, so as to only check the modified code, instead of building the full table or the entire DAG. :::info @@ -95,11 +101,15 @@ Automatically test your semantic nodes (metrics, semantic models, and saved quer To do this, add the command `dbt sl validate --select state:modified+` in the CI job. This ensures the validation of modified semantic nodes and their downstream dependencies. + + +#### Benefits - Testing semantic nodes in a CI job supports deferral and selection of semantic nodes. - It allows you to catch issues early in the development process and deliver high-quality data to your end users. - Semantic validation executes an explain query in the data warehouse for semantic nodes to ensure the generated SQL will execute. - For semantic nodes and models that aren't downstream of modified models, dbt Cloud defers to the production models +### Set up semantic validations in your CI job To learn how to set this up, refer to the following steps: 1. Navigate to the **Job setting** page and click **Edit**. diff --git a/website/docs/docs/deploy/continuous-integration.md b/website/docs/docs/deploy/continuous-integration.md index 4e152b0a97e..c738e641a5b 100644 --- a/website/docs/docs/deploy/continuous-integration.md +++ b/website/docs/docs/deploy/continuous-integration.md @@ -5,7 +5,7 @@ description: "You can set up continuous integration (CI) checks to test every si pagination_next: "docs/deploy/advanced-ci" --- -To implement a continuous integration (CI) workflow in dbt Cloud, you can set up automation that tests code changes by running [CI jobs](/docs/deploy/ci-jobs) before merging to production. dbt Cloud tracks the state of what’s running in your production environment so, when you run a CI job, only the modified data assets in your pull request (PR) and their downstream dependencies are built and tested in a staging schema. You can also view the status of the CI checks (tests) directly from within the PR; this information is posted to your Git provider as soon as a CI job completes. Additionally, you can enable settings in your Git provider that allow PRs only with successful CI checks be approved for merging. +To implement a continuous integration (CI) workflow in dbt Cloud, you can set up automation that tests code changes by running [CI jobs](/docs/deploy/ci-jobs) before merging to production. dbt Cloud tracks the state of what’s running in your production environment so, when you run a CI job, only the modified data assets in your pull request (PR) and their downstream dependencies are built and tested in a staging schema. You can also view the status of the CI checks (tests) directly from within the PR; this information is posted to your Git provider as soon as a CI job completes. Additionally, you can enable settings in your Git provider that allow PRs only with successful CI checks to be approved for merging. @@ -13,11 +13,11 @@ Using CI helps: - Provide increased confidence and assurances that project changes will work as expected in production. - Reduce the time it takes to push code changes to production, through build and test automation, leading to better business outcomes. -- Allow organizations to make code changes in a standardized and governed way that ensure code quality without sacrificing speed. +- Allow organizations to make code changes in a standardized and governed way that ensures code quality without sacrificing speed. ## How CI works -When you [set up CI jobs](/docs/deploy/ci-jobs#set-up-ci-jobs), dbt Cloud listens for notification from your Git provider indicating that a new PR has been opened or updated with new commits. When dbt Cloud receives one of these notifications, it enqueues a new run of the CI job. +When you [set up CI jobs](/docs/deploy/ci-jobs#set-up-ci-jobs), dbt Cloud listens for a notification from your Git provider indicating that a new PR has been opened or updated with new commits. When dbt Cloud receives one of these notifications, it enqueues a new run of the CI job. dbt Cloud builds and tests models, semantic models, metrics, and saved queries affected by the code change in a temporary schema, unique to the PR. This process ensures that the code builds without error and that it matches the expectations as defined by the project's dbt tests. The unique schema name follows the naming convention `dbt_cloud_pr__` (for example, `dbt_cloud_pr_1862_1704`) and can be found in the run details for the given run, as shown in the following image: @@ -31,16 +31,16 @@ dbt Cloud deletes the temporary schema from your  w The [dbt Cloud scheduler](/docs/deploy/job-scheduler) executes CI jobs differently from other deployment jobs in these important ways: -- **Concurrent CI checks** — CI runs triggered by the same dbt Cloud CI job execute concurrently (in parallel), when appropriate. -- **Smart cancellation of stale builds** — Automatically cancels stale, in-flight CI runs when there are new commits to the PR. -- **Run slot treatment** — CI runs don't consume a run slot. -- **SQL linting** — When enabled, automatically lints all SQL files in your project as a run step before your CI job builds. +- [**Concurrent CI checks**](#concurrent-ci-checks) — CI runs triggered by the same dbt Cloud CI job execute concurrently (in parallel), when appropriate. +- [**Smart cancellation of stale builds**](#smart-cancellation-of-stale-builds) — Automatically cancels stale, in-flight CI runs when there are new commits to the PR. +- [**Run slot treatment**](#run-slot-treatment) — CI runs don't consume a run slot. +- [**SQL linting**](#sql-linting) — When enabled, automatically lints all SQL files in your project as a run step before your CI job builds. ### Concurrent CI checks When you have teammates collaborating on the same dbt project creating pull requests on the same dbt repository, the same CI job will get triggered. Since each run builds into a dedicated, temporary schema that’s tied to the pull request, dbt Cloud can safely execute CI runs _concurrently_ instead of _sequentially_ (differing from what is done with deployment dbt Cloud jobs). Because no one needs to wait for one CI run to finish before another one can start, with concurrent CI checks, your whole team can test and integrate dbt code faster. -Below describes the conditions when CI checks are run concurrently and when they’re not: +The following describes the conditions when CI checks are run concurrently and when they’re not: - CI runs with different PR numbers execute concurrently. - CI runs with the _same_ PR number and _different_ commit SHAs execute serially because they’re building into the same schema. dbt Cloud will run the latest commit and cancel any older, stale commits. For details, refer to [Smart cancellation of stale builds](#smart-cancellation). @@ -56,10 +56,19 @@ When you push a new commit to a PR, dbt Cloud enqueues a new CI run for the late CI runs don't consume run slots. This guarantees a CI check will never block a production run. -### SQL linting +### SQL linting -When enabled for your CI job, dbt invokes [SQLFluff](https://sqlfluff.com/) which is a modular and configurable SQL linter that warns you of complex functions, syntax, formatting, and compilation errors. By default, it lints all the changed SQL files in your project (compared to the last deferred production state). +Available on [dbt Cloud release tracks](/docs/dbt-versions/cloud-release-tracks) and dbt Cloud Team or Enterprise accounts. -If the linter runs into errors, you can specify whether dbt should stop running the job on error or continue running it on error. When failing jobs, it helps reduce compute costs by avoiding builds for pull requests that don't meet your SQL code quality CI check. +When [enabled for your CI job](/docs/deploy/ci-jobs#set-up-ci-jobs), dbt invokes [SQLFluff](https://sqlfluff.com/) which is a modular and configurable SQL linter that warns you of complex functions, syntax, formatting, and compilation errors. By default, it lints all the changed SQL files in your project (compared to the last deferred production state). -You can use [SQLFluff Configuration Files](https://docs.sqlfluff.com/en/stable/configuration/setting_configuration.html#configuration-files) to override the default linting behavior in dbt. Create an `.sqlfluff` configuration file in your project, add your linting rules to it, and dbt Cloud will use them when linting. For complete details, refer to [Custom Usage](https://docs.sqlfluff.com/en/stable/gettingstarted.html#custom-usage) in the SQLFluff documentation. +If the linter runs into errors, you can specify whether dbt should stop running the job on error or continue running it on error. When failing jobs, it helps reduce compute costs by avoiding builds for pull requests that don't meet your SQL code quality CI check. + +#### To configure SQLFluff linting: +You can optionally configure SQLFluff linting rules to override default linting behavior. + +- Use [SQLFluff Configuration Files](https://docs.sqlfluff.com/en/stable/configuration/setting_configuration.html#configuration-files) to override the default linting behavior in dbt. +- Create a `.sqlfluff` configuration file in your project, add your linting rules to it, and dbt Cloud will use them when linting. + - When configuring, you can use `dbt` as the templater (for example, `templater = dbt`) + - If you’re using the dbt Cloud IDE, dbt Cloud CLI, or any other editor, refer to [Customize linting](/docs/cloud/dbt-cloud-ide/lint-format#customize-linting) for guidance on how to add the dbt-specific (or dbtonic) linting rules we use for own project. +- For complete details, refer to [Custom Usage](https://docs.sqlfluff.com/en/stable/gettingstarted.html#custom-usage) in the SQLFluff documentation. diff --git a/website/docs/docs/deploy/deploy-environments.md b/website/docs/docs/deploy/deploy-environments.md index 088ecb0d841..e8c7816979a 100644 --- a/website/docs/docs/deploy/deploy-environments.md +++ b/website/docs/docs/deploy/deploy-environments.md @@ -29,13 +29,13 @@ We highly recommend using the `Production` environment type for the final, sourc To create a new dbt Cloud deployment environment, navigate to **Deploy** -> **Environments** and then click **Create Environment**. Select **Deployment** as the environment type. The option will be greyed out if you already have a development environment. - + ### Set as production environment In dbt Cloud, each project can have one designated deployment environment, which serves as its production environment. This production environment is _essential_ for using features like dbt Explorer and cross-project references. It acts as the source of truth for the project's production state in dbt Cloud. - + ### Semantic Layer diff --git a/website/docs/docs/deploy/job-commands.md b/website/docs/docs/deploy/job-commands.md index abea687f4db..29c98f1916c 100644 --- a/website/docs/docs/deploy/job-commands.md +++ b/website/docs/docs/deploy/job-commands.md @@ -28,7 +28,7 @@ Every job invocation automatically includes the [`dbt deps`](/reference/commands **Job outcome** — During a job run, the built-in commands are "chained" together. This means if one of the run steps in the chain fails, then the next commands aren't executed, and the entire job fails with an "Error" job status. - + ### Checkbox commands @@ -42,14 +42,36 @@ For every job, you have the option to select the [Generate docs on run](/docs/co You can add or remove as many dbt commands as necessary for every job. However, you need to have at least one dbt command. There are few commands listed as "dbt Cloud CLI" or "dbt Core" in the [dbt Command reference page](/reference/dbt-commands) page. This means they are meant for use in dbt Core or dbt Cloud CLI, and not in dbt Cloud IDE. - :::tip Using selectors -Use [selectors](/reference/node-selection/syntax) as a powerful way to select and execute portions of your project in a job run. For example, to run tests for one_specific_model, use the selector: `dbt test --select one_specific_model`. The job will still run if a selector doesn't match any models. +Use [selectors](/reference/node-selection/syntax) as a powerful way to select and execute portions of your project in a job run. For example, to run tests for `one_specific_model`, use the selector: `dbt test --select one_specific_model`. The job will still run if a selector doesn't match any models. ::: -**Job outcome** — During a job run, the commands are "chained" together and executed as run steps. If one of the run steps in the chain fails, then the subsequent steps aren't executed, and the job will fail. +#### Compare changes custom commands +For users that have Advanced CI's [compare changes](/docs/deploy/advanced-ci#compare-changes) feature enabled and selected the **dbt compare** checkbox, you can add custom dbt commands to optimize running the comparison (for example, to exclude specific large models, or groups of models with tags). Running comparisons on large models can significantly increase the time it takes for CI jobs to complete. + + + +The following examples highlight how you can customize the dbt compare command box: + +- Exclude the large `fct_orders` model from the comparison to run a CI job on fewer or smaller models and reduce job time/resource consumption. Use the following command: + + ```sql + --select state:modified --exclude fct_orders + ``` +- Exclude models based on tags for scenarios like when models share a common feature or function. Use the following command: + + ```sql + --select state modified --exclude tag:tagname_a tag:tagname_b + ``` +- Include models that were directly modified and also those one step downstream using the `modified+1` selector. Use the following command: + ```sql + --select state:modified+1 + ``` + +#### Job outcome +During a job run, the commands are "chained" together and executed as run steps. If one of the run steps in the chain fails, then the subsequent steps aren't executed, and the job will fail. In the following example image, the first four run steps are successful. However, if the fifth run step (`dbt run --select state:modified+ --full-refresh --fail-fast`) fails, then the next run steps aren't executed, and the entire job fails. The failed job returns a non-zero [exit code](/reference/exit-codes) and "Error" job status: diff --git a/website/docs/docs/deploy/job-notifications.md b/website/docs/docs/deploy/job-notifications.md index 62c51461ab2..fb4b5f557e6 100644 --- a/website/docs/docs/deploy/job-notifications.md +++ b/website/docs/docs/deploy/job-notifications.md @@ -52,11 +52,13 @@ You can receive email alerts about jobs by configuring the dbt Cloud email notif You can receive Slack alerts about jobs by setting up the Slack integration and then configuring the dbt Cloud Slack notification settings. dbt Cloud integrates with Slack via OAuth to ensure secure authentication. :::note +Virtual Private Cloud (VPC) admins must [contact support](mailto:support@getdbt.com) to complete the Slack integration. + If there has been a change in user roles or Slack permissions where you no longer have access to edit a configured Slack channel, please [contact support](mailto:support@getdbt.com) for assistance. ::: ### Prerequisites -- You must be an administrator of the Slack workspace. +- You must be a Slack Workspace Owner. - You must be an account admin to configure Slack notifications in dbt Cloud. For more details, refer to [Users and licenses](/docs/cloud/manage-access/seats-and-users). - The integration only supports _public_ channels in the Slack workspace. diff --git a/website/docs/docs/deploy/merge-jobs.md b/website/docs/docs/deploy/merge-jobs.md index 8b2900661fa..a187e3992f8 100644 --- a/website/docs/docs/deploy/merge-jobs.md +++ b/website/docs/docs/deploy/merge-jobs.md @@ -5,7 +5,7 @@ description: "Learn how to trigger a dbt job run when a Git pull request merges. --- -You can set up a merge job to implement a continuous development (CD) workflow in dbt Cloud. The merge job triggers a dbt job to run when someone merges Git pull requests into production. This workflow creates a seamless development experience where changes made in code will automatically update production data. Also, you can use this workflow for running `dbt compile` to update your environment's manifest so subsequent CI job runs are more performant. +You can set up a merge job to implement a continuous deployment (CD) workflow in dbt Cloud. The merge job triggers a dbt job to run when someone merges Git pull requests into production. This workflow creates a seamless development experience where changes made in code will automatically update production data. Also, you can use this workflow for running `dbt compile` to update your environment's manifest so subsequent CI job runs are more performant. By using CD in dbt Cloud, you can take advantage of deferral to build only the edited model and any downstream changes. With merge jobs, state will be updated almost instantly, always giving the most up-to-date state information in [dbt Explorer](/docs/collaborate/explore-projects). @@ -62,4 +62,4 @@ The following is an example of creating a new **Code pushed** trigger in Azure D - \ No newline at end of file + diff --git a/website/docs/docs/deploy/model-notifications.md b/website/docs/docs/deploy/model-notifications.md index a6d4c467f0b..24bbc2295c6 100644 --- a/website/docs/docs/deploy/model-notifications.md +++ b/website/docs/docs/deploy/model-notifications.md @@ -3,8 +3,6 @@ title: "Model notifications" description: "While a job is running, receive email notifications in real time about any issues with your models and tests. " --- -# Model notifications - Set up dbt to notify the appropriate model owners through email about issues as soon as they occur, while the job is still running. Model owners can specify which statuses to receive notifications about: - `Success` and `Fails` for models @@ -12,28 +10,26 @@ Set up dbt to notify the appropriate model owners through email about issues as With model-level notifications, model owners can be the first ones to know about issues before anyone else (like the stakeholders). -:::info Beta feature - -This feature is currently available in [beta](/docs/dbt-versions/product-lifecycles#dbt-cloud) to a limited group of users and is gradually being rolled out. If you're in the beta, please contact the Support team at support@getdbt.com for assistance or questions. - -::: - To be timely and keep the number of notifications to a reasonable amount when multiple models or tests trigger them, dbt observes the following guidelines when notifying the owners: - Send a notification to each unique owner/email during a job run about any models (with status of failure/success) or tests (with status of warning/failure/success). Each owner receives only one notification, the initial one. -- Don't send any notifications about subsequent models or tests while a dbt job is still running. -- At the end of a job run, each owner receives a notification, for each of the statuses they specified to be notified about, with a list of models and tests that have that status. +- No notifications sent about subsequent models or tests while a dbt job is still running. +- Each owner/user who subscribes to notifications for one or more statuses (like failure, success, warning) will receive only _one_ email notification at the end of the job run. +- The email includes a consolidated list of all models or tests that match the statuses the user subscribed to, instead of sending separate emails for each status. Create configuration YAML files in your project for dbt to send notifications about the status of your models and tests. ## Prerequisites - Your dbt Cloud administrator has [enabled the appropriate account setting](#enable-access-to-model-notifications) for you. -- Your environment(s) must be on ["Versionless"](/docs/dbt-versions/versionless-cloud). - +- Your environment(s) must be on a [release track](/docs/dbt-versions/cloud-release-tracks) instead of a legacy dbt Core version. ## Configure groups -Add your group configuration in either the `dbt_project.yml` or `groups.yml` file. For example: +Define your groups in any `.yml` file in your [models directory](/reference/project-configs/model-paths). Each group must have a single email address specified — multiple email fields or lists aren't supported. + +The following example shows how to define groups in a `groups.yml` file. + + ```yml version: 2 @@ -42,22 +38,26 @@ groups: - name: finance description: "Models related to the finance department" owner: - # 'name' or 'email' is required + # Email is required to receive model-level notifications, additional properties are also allowed. name: "Finance Team" email: finance@dbtlabs.com - slack: finance-data + favorite_food: donuts - name: marketing description: "Models related to the marketing department" owner: name: "Marketing Team" email: marketing@dbtlabs.com - slack: marketing-data + favorite_food: jaffles ``` -## Set up models + + +## Attach groups to models -Set up your model configuration in either the `dbt_project.yml` or `groups.yml` file; doing this automatically sets up notifications for tests, too. For example: +Attach groups to models as you would any other config, in either the `dbt_project.yml` or `whatever.yml` files. For example: + + ```yml version: 2 @@ -74,6 +74,34 @@ models: group: marketing ``` + + +By assigning groups in the `dbt_project.yml` file, you can capture all models in a subdirectory at once. + +In this example, model notifications related to staging models go to the data engineering group, `marts/sales` models to the finance team, and `marts/campaigns` models to the marketing team. + + + +```yml +config-version: 2 +name: "jaffle_shop" + +[...] + +models: + jaffle_shop: + staging: + +group: data_engineering + marts: + sales: + +group: finance + campaigns: + +group: marketing + +``` + + +Attaching a group to a model also encompasses its tests, so you will also receive notifications for a model's test failures. ## Enable access to model notifications @@ -82,6 +110,6 @@ Provide dbt Cloud account members the ability to configure and receive alerts ab To use model-level notifications, your dbt Cloud account must have access to the feature. Ask your dbt Cloud administrator to enable this feature for account members by following these steps: 1. Navigate to **Notification settings** from your profile name in the sidebar (lower left-hand side). -1. From **Email notications**, enable the setting **Enable group/owner notifications on models** under the **Model notifications** section. Then, specify which statuses to receive notifications about (Success, Warning, and/or Fails). +1. From **Email notifications**, enable the setting **Enable group/owner notifications on models** under the **Model notifications** section. Then, specify which statuses to receive notifications about (Success, Warning, and/or Fails). diff --git a/website/docs/docs/deploy/monitor-jobs.md b/website/docs/docs/deploy/monitor-jobs.md index 1cbba23161e..40298f0cdbe 100644 --- a/website/docs/docs/deploy/monitor-jobs.md +++ b/website/docs/docs/deploy/monitor-jobs.md @@ -13,7 +13,7 @@ This portion of our documentation will go over dbt Cloud's various capabilities - [Run visibility](/docs/deploy/run-visibility) — View your run history to help identify where improvements can be made to scheduled jobs. - [Retry jobs](/docs/deploy/retry-jobs) — Rerun your errored jobs from start or the failure point. - [Job notifications](/docs/deploy/job-notifications) — Receive email or Slack notifications when a job run succeeds, encounters warnings, fails, or is canceled. -- [Model notifications](/docs/deploy/model-notifications) — Receive email notifications about any issues encountered by your models and tests as soon as they occur while running a job. +- [Model notifications](/docs/deploy/model-notifications) — Receive email notifications about any issues encountered by your models and tests as soon as they occur while running a job. - [Webhooks](/docs/deploy/webhooks) — Use webhooks to send events about your dbt jobs' statuses to other systems. - [Leverage artifacts](/docs/deploy/artifacts) — dbt Cloud generates and saves artifacts for your project, which it uses to power features like creating docs for your project and reporting freshness of your sources. - [Source freshness](/docs/deploy/source-freshness) — Monitor data governance by enabling snapshots to capture the freshness of your data sources. diff --git a/website/docs/docs/deploy/run-visibility.md b/website/docs/docs/deploy/run-visibility.md index 255882d066f..77db0e65fbb 100644 --- a/website/docs/docs/deploy/run-visibility.md +++ b/website/docs/docs/deploy/run-visibility.md @@ -33,7 +33,7 @@ An example of a completed run with a configuration for a [job completion trigger You can view or download in-progress and historical logs for your dbt runs. This makes it easier for the team to debug errors more efficiently. - + ### Lineage tab diff --git a/website/docs/docs/deploy/webhooks.md b/website/docs/docs/deploy/webhooks.md index ffea38b5b84..52ce2a1fe56 100644 --- a/website/docs/docs/deploy/webhooks.md +++ b/website/docs/docs/deploy/webhooks.md @@ -36,7 +36,7 @@ You can also check out the free [dbt Fundamentals course](https://learn.getdbt.c ## Create a webhook subscription {#create-a-webhook-subscription} -From your **Account Settings** in dbt Cloud (using the gear menu in the top right corner), click **Create New Webhook** in the **Webhooks** section. You can find the appropriate dbt Cloud access URL for your region and plan with [Regions & IP addresses](/docs/cloud/about-cloud/access-regions-ip-addresses). +Navigate to **Account settings** in dbt Cloud (by clicking your account name from the left side panel), and click **Create New Webhook** in the **Webhooks** section. You can find the appropriate dbt Cloud access URL for your region and plan with [Regions & IP addresses](/docs/cloud/about-cloud/access-regions-ip-addresses). To configure your new webhook: diff --git a/website/docs/docs/use-dbt-semantic-layer/exports.md b/website/docs/docs/use-dbt-semantic-layer/exports.md index af8e0dce1cd..d5285c8f779 100644 --- a/website/docs/docs/use-dbt-semantic-layer/exports.md +++ b/website/docs/docs/use-dbt-semantic-layer/exports.md @@ -180,7 +180,7 @@ If exports aren't needed, you can set the value(s) to `FALSE` (`DBT_INCLUDE_SAVE - + 1. Click **Deploy** in the top navigation bar and choose **Environments**. diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-cache.md b/website/docs/docs/use-dbt-semantic-layer/sl-cache.md index 0c6387959a3..27ffe97a951 100644 --- a/website/docs/docs/use-dbt-semantic-layer/sl-cache.md +++ b/website/docs/docs/use-dbt-semantic-layer/sl-cache.md @@ -22,7 +22,7 @@ While you can use caching to speed up your queries and reduce compute time, know ## Prerequisites - dbt Cloud [Team or Enterprise](https://www.getdbt.com/) plan. -- dbt Cloud environments that are ["Versionless"](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless). +- dbt Cloud environments must be on [release tracks](/docs/dbt-versions/cloud-release-tracks) and not legacy dbt Core versions. - A successful job run and [production environment](/docs/deploy/deploy-environments#set-as-production-environment). - For declarative caching, you need to have [exports](/docs/use-dbt-semantic-layer/exports) defined in your [saved queries](/docs/build/saved-queries) YAML configuration file. diff --git a/website/docs/faqs/Accounts/change-billing.md b/website/docs/faqs/Accounts/change-billing.md index 11290728c98..2b2aa607c16 100644 --- a/website/docs/faqs/Accounts/change-billing.md +++ b/website/docs/faqs/Accounts/change-billing.md @@ -6,6 +6,6 @@ id: change-billing --- -If you want to change your account's credit card details, select the gear menu in the upper right corner of dbt Cloud. Go to Account Settings → Billing → Payment Information. Enter the new credit card details on the respective fields then click on **Update payment information**. Only the _account owner_ can make this change. +If you want to change your account's credit card details, go to the left side panel, click **Account settings** → **Billing** → scroll to **Payment information**. Enter the new credit card details on the respective fields then click on **Update payment information**. Only the _account owner_ can make this change. To change your billing name or location address, send our Support team a message at support@getdbt.com with the newly updated information, and we can make that change for you! diff --git a/website/docs/faqs/Accounts/change-users-license.md b/website/docs/faqs/Accounts/change-users-license.md index 8755b946126..ae44414e5f9 100644 --- a/website/docs/faqs/Accounts/change-users-license.md +++ b/website/docs/faqs/Accounts/change-users-license.md @@ -8,12 +8,12 @@ id: change-user-license To change the license type for a user from `developer` to `read-only` or `IT` in dbt Cloud, you must be an account owner or have admin privileges. You might make this change to free up a billable seat but retain the user’s access to view the information in the dbt Cloud account. -1. From dbt Cloud, click the gear icon at the top right and select **Account Settings**. +1. From dbt Cloud, click on your account name in the left side menu and, select **Account settings**. - + 2. In **Account Settings**, select **Users** under **Teams**. -3. Select the user you want to remove, and click **Edit** in the bottom of their profile. +3. Select the user you want to remove and click **Edit** in the bottom of their profile. 4. For the **License** option, choose **Read-only** or **IT** (from **Developer**), and click **Save**. - + diff --git a/website/docs/faqs/Accounts/delete-users.md b/website/docs/faqs/Accounts/delete-users.md index a7e422fd82c..1efbb018242 100644 --- a/website/docs/faqs/Accounts/delete-users.md +++ b/website/docs/faqs/Accounts/delete-users.md @@ -8,15 +8,15 @@ id: delete-users To delete a user in dbt Cloud, you must be an account owner or have admin privileges. If the user has a `developer` license type, this will open up their seat for another user or allow the admins to lower the total number of seats. -1. From dbt Cloud, click the gear icon at the top right and select **Account Settings**. +1. From dbt Cloud, click on your account name in the left side menu and, select **Account settings**. - + 2. In **Account Settings**, select **Users** under **Teams**. 3. Select the user you want to delete, then click **Edit**. 4. Click **Delete** in the bottom left. Click **Confirm Delete** to immediately delete the user without additional password prompts. This action cannot be undone. However, you can re-invite the user with the same information if the deletion was made in error. - + If you are on a **Teams** plan and you are deleting users to reduce the number of billable seats, you also need to take these steps to lower the license count: 1. In **Account Settings**, select **Billing**. diff --git a/website/docs/faqs/Docs/long-descriptions.md b/website/docs/faqs/Docs/long-descriptions.md index ef410df0517..e984a6e78c8 100644 --- a/website/docs/faqs/Docs/long-descriptions.md +++ b/website/docs/faqs/Docs/long-descriptions.md @@ -32,4 +32,3 @@ If you need more than a sentence to explain a model, you can: ``` 3. Use a [docs block](/docs/build/documentation#using-docs-blocks) to write the description in a separate Markdown file. -b diff --git a/website/docs/faqs/Environments/custom-branch-settings.md b/website/docs/faqs/Environments/custom-branch-settings.md index 70052488ac6..6e998b267d8 100644 --- a/website/docs/faqs/Environments/custom-branch-settings.md +++ b/website/docs/faqs/Environments/custom-branch-settings.md @@ -27,7 +27,7 @@ For example, if you want to use the `develop` branch of a connected repository: - Enter **develop** as the name of your custom branch - Click **Save** - + ## Deployment diff --git a/website/docs/faqs/Environments/delete-environment-job.md b/website/docs/faqs/Environments/delete-environment-job.md index eb9ac511a7c..5b167b6df13 100644 --- a/website/docs/faqs/Environments/delete-environment-job.md +++ b/website/docs/faqs/Environments/delete-environment-job.md @@ -18,7 +18,7 @@ To delete a job or multiple jobs in dbt Cloud: 4. Scroll to the bottom of the page and click **Delete** to delete the job.
- +
Delete a job
@@ -35,10 +35,7 @@ Deleting an environment automatically deletes its associated job(s). If you want 3. Click **Settings** on the top right of the page and then click **Edit**. 4. Scroll to the bottom of the page and click **Delete** to delete the environment.
-
- -
Delete an environment
-
+ 5. Confirm your action in the **Confirm Delete** pop-up by clicking **Confirm Delete** in the bottom right to delete the environment immediately. This action cannot be undone. However, you can create a new environment with the same information if the deletion was made in error.

diff --git a/website/docs/faqs/Git/git-migration.md b/website/docs/faqs/Git/git-migration.md index 156227d59ae..7d7a503c16a 100644 --- a/website/docs/faqs/Git/git-migration.md +++ b/website/docs/faqs/Git/git-migration.md @@ -16,7 +16,7 @@ To migrate from one git provider to another, refer to the following steps to avo 2. Go back to dbt Cloud and set up your [integration for the new git provider](/docs/cloud/git/connect-github), if needed. 3. Disconnect the old repository in dbt Cloud by going to **Account Settings** and then **Projects**. Click on the **Repository** link, then click **Edit** and **Disconnect**. - + 4. On the same page, connect to the new git provider repository by clicking **Configure Repository** - If you're using the native integration, you may need to OAuth to it. diff --git a/website/docs/faqs/Git/github-permissions.md b/website/docs/faqs/Git/github-permissions.md index 075343e0c5e..c244b6742b9 100644 --- a/website/docs/faqs/Git/github-permissions.md +++ b/website/docs/faqs/Git/github-permissions.md @@ -40,7 +40,7 @@ Disconnect the GitHub and dbt Cloud integration in dbt Cloud. 6. Return to your **Project details** page and reconnect your repository by clicking the **Configure Repository** link. 7. Configure your repository and click **Save** - + ## Support If you've tried these workarounds and are still experiencing this behavior — reach out to the [dbt Support](mailto:support@getdbt.com) team and we'll be happy to help! diff --git a/website/docs/faqs/Git/gitignore.md b/website/docs/faqs/Git/gitignore.md index 4386a27d4f2..f5892b30b83 100644 --- a/website/docs/faqs/Git/gitignore.md +++ b/website/docs/faqs/Git/gitignore.md @@ -47,9 +47,9 @@ For more info on `gitignore` syntax, refer to the [Git docs](https://git-scm.com 11. Return to the dbt Cloud IDE and use the **Change Branch** button, to switch to the main branch of the project. 12. Once the branch has changed, click the **Pull from remote** button to pull in all the changes. -13. Verify the changes by making sure the files/folders in the `.gitignore `file are in italics. +13. Verify the changes by making sure the files/folders in the `.gitignore` file are in italics. - + ### Fix in the git provider diff --git a/website/docs/faqs/Git/managed-repo.md b/website/docs/faqs/Git/managed-repo.md index 17b75256fb6..c357fce112c 100644 --- a/website/docs/faqs/Git/managed-repo.md +++ b/website/docs/faqs/Git/managed-repo.md @@ -7,4 +7,8 @@ id: managed-repo dbt Labs can send your managed repository through a ZIP file in its current state for you to push up to a git provider. After that, you'd just need to switch over to the [repo in your project](/docs/cloud/git/import-a-project-by-git-url) to point to the new repository. -When you're ready to do this, [contact the dbt Labs Support team](mailto:support@getdbt.com) with your request and your managed repo URL, which you can find by navigating to your project setting. To find project settings, click the gear icon in the upper right, select **Account settings**, click **Projects**, and then select your project. Under **Repository** in the project details page, you can find your managed repo URL. +When you're ready to do this, [contact the dbt Labs Support team](mailto:support@getdbt.com) with your request and your managed repo URL, which you can find by navigating to your project setting. To find project settings: + +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. +2. Click **Projects**, and then select your project. +3. Under **Repository** in the project details page, you can find your managed repo URL. diff --git a/website/docs/faqs/Project/dbt-source-freshness.md b/website/docs/faqs/Project/dbt-source-freshness.md index e2554579ffc..61bd5d035ba 100644 --- a/website/docs/faqs/Project/dbt-source-freshness.md +++ b/website/docs/faqs/Project/dbt-source-freshness.md @@ -11,4 +11,4 @@ The `dbt source freshness` command will output a pass/warning/error status for e Additionally, dbt will write the freshness results to a file in the `target/` directory called `sources.json` by default. You can also override this destination, use the `-o` flag to the `dbt source freshness` command. -After enabling source freshness within a job, configure [Artifacts](/docs/deploy/artifacts) in your **Project Details** page, which you can find by clicking the gear icon and then selecting **Account settings**. You can see the current status for source freshness by clicking **View Sources** in the job page. +After enabling source freshness within a job, configure [Artifacts](/docs/deploy/artifacts) in your **Project Details** page, which you can find by selectng your account name on the left side menu in dbt Cloud and clicking **Account settings**. You can see the current status for source freshness by clicking **View Sources** in the job page. diff --git a/website/docs/faqs/Project/delete-a-project.md b/website/docs/faqs/Project/delete-a-project.md index 5fde3fee9cd..36c6bf4f160 100644 --- a/website/docs/faqs/Project/delete-a-project.md +++ b/website/docs/faqs/Project/delete-a-project.md @@ -7,12 +7,12 @@ id: delete-a-project --- To delete a project in dbt Cloud, you must be the account owner or have admin privileges. -1. From dbt Cloud, click the gear icon at the top right corner and select **Account Settings**. +1. From dbt Cloud, click on your account name in the left side menu and select **Account settings**. - + 2. In **Account Settings**, select **Projects**. Click the project you want to delete from the **Projects** page. 3. Click the edit icon in the lower right-hand corner of the **Project Details**. A **Delete** option will appear on the left side of the same details view. 4. Select **Delete**. Confirm the action to immediately delete the user without additional password prompts. There will be no account password prompt, and the project is deleted immediately after confirmation. Once a project is deleted, this action cannot be undone. - + diff --git a/website/docs/faqs/Troubleshooting/failed-snowflake-oauth-connection.md b/website/docs/faqs/Troubleshooting/failed-snowflake-oauth-connection.md new file mode 100644 index 00000000000..84ef49d212c --- /dev/null +++ b/website/docs/faqs/Troubleshooting/failed-snowflake-oauth-connection.md @@ -0,0 +1,31 @@ +--- +title: Receiving a `Failed to connect to DB` error when connecting to Snowflake +description: "Edit your OAuth Security integration when you see error" +sidebar_label: 'Receiving `Failed to connect to database` error' +--- + +1. If you see the following error: + + ```text + Failed to connect to DB: xxxxxxx.snowflakecomputing.com:443. The role requested in the connection, or the default role if none was requested in the connection ('xxxxx'), is not listed in the Access Token or was filtered. + Please specify another role, or contact your OAuth Authorization server administrator. + ``` + +2. Edit your OAuth Security integration and explicitly specify this scope mapping attribute: + + ```sql + ALTER INTEGRATION SET EXTERNAL_OAUTH_SCOPE_MAPPING_ATTRIBUTE = 'scp'; + ``` + +You can read more about this error in [Snowflake's documentation](https://community.snowflake.com/s/article/external-custom-oauth-error-the-role-requested-in-the-connection-is-not-listed-in-the-access-token). + +---- + +1. If you see the following error: + + ```text + Failed to connect to DB: xxxxxxx.snowflakecomputing.com:443. Incorrect username or password was specified. + ``` + + * **Unique email addresses** — Each user in Snowflake must have a unique email address. You can't have multiple users (for example, a human user and a service account) using the same email, such as `alice@acme.com`, to authenticate to Snowflake. + * **Match email addresses with identity provider** — The email address of your Snowflake user must exactly match the email address you use to authenticate with your Identity Provider (IdP). For example, if your Snowflake user's email is `alice@acme.com` but you log in to Entra or Okta with `alice_adm@acme.com`, this mismatch can cause an error. diff --git a/website/docs/guides/adapter-creation.md b/website/docs/guides/adapter-creation.md index 278e2a9fe14..37ef5ec0412 100644 --- a/website/docs/guides/adapter-creation.md +++ b/website/docs/guides/adapter-creation.md @@ -666,7 +666,7 @@ In order to enable the [`dbt init` command](/reference/commands/init) to prompt See examples: -- [dbt-postgres](https://github.com/dbt-labs/dbt-core/blob/main/plugins/postgres/dbt/include/postgres/profile_template.yml) +- [dbt-postgres](https://github.com/dbt-labs/dbt-postgres/blob/main/dbt/include/postgres/profile_template.yml) - [dbt-redshift](https://github.com/dbt-labs/dbt-redshift/blob/main/dbt/include/redshift/profile_template.yml) - [dbt-snowflake](https://github.com/dbt-labs/dbt-snowflake/blob/main/dbt/include/snowflake/profile_template.yml) - [dbt-bigquery](https://github.com/dbt-labs/dbt-bigquery/blob/main/dbt/include/bigquery/profile_template.yml) @@ -1345,8 +1345,6 @@ Breaking this down: - Implementation instructions: -- Future plans - - Contributor recognition (if applicable) diff --git a/website/docs/guides/athena-qs.md b/website/docs/guides/athena-qs.md new file mode 100644 index 00000000000..b1933bdd076 --- /dev/null +++ b/website/docs/guides/athena-qs.md @@ -0,0 +1,334 @@ +--- +title: "Quickstart for dbt Cloud and Amazon Athena" +id: "athena" +# time_to_complete: '30 minutes' commenting out until we test +level: 'Beginner' +icon: 'athena' +hide_table_of_contents: true +tags: ['Amazon','Athena', 'dbt Cloud','Quickstart'] +recently_updated: true +--- + +
+ +## Introduction + +In this quickstart guide, you'll learn how to use dbt Cloud with Amazon Athena. It will show you how to: + +- Create an S3 bucket for Athena query results. +- Creat an Athena database. +- Access sample data in a public dataset. +- Connect dbt Cloud to Amazon Athena. +- Take a sample query and turn it into a model in your dbt project. A model in dbt is a select statement. +- Add tests to your models. +- Document your models. +- Schedule a job to run. + +:::tip Videos for you +You can check out [dbt Fundamentals](https://learn.getdbt.com/courses/dbt-fundamentals) for free if you're interested in course learning with videos. +::: + +### Prerequisites​ + +- You have a [dbt Cloud account](https://www.getdbt.com/signup/). +- You have an [AWS account](https://aws.amazon.com/). +- You have set up [Amazon Athena](https://docs.aws.amazon.com/athena/latest/ug/getting-started.html). + +### Related content + +- Learn more with [dbt Learn courses](https://learn.getdbt.com) +- [CI jobs](/docs/deploy/continuous-integration) +- [Deploy jobs](/docs/deploy/deploy-jobs) +- [Job notifications](/docs/deploy/job-notifications) +- [Source freshness](/docs/deploy/source-freshness) + +## Getting started + +For the following guide you can use an existing S3 bucket or [create a new one](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html). + +Download the following CSV files (the Jaffle Shop sample data) and upload them to your S3 bucket: +- [jaffle_shop_customers.csv](https://dbt-tutorial-public.s3-us-west-2.amazonaws.com/jaffle_shop_customers.csv) +- [jaffle_shop_orders.csv](https://dbt-tutorial-public.s3-us-west-2.amazonaws.com/jaffle_shop_orders.csv) +- [stripe_payments.csv](https://dbt-tutorial-public.s3-us-west-2.amazonaws.com/stripe_payments.csv) + + +## Configure Amazon Athena + +1. Log into your AWS account and navigate to the **Athena console**. + - If this is your first time in the Athena console (in your current AWS Region), click **Explore the query editor** to open the query editor. Otherwise, Athena opens automatically in the query editor. +1. Open **Settings** and find the **Location of query result box** field. + 1. Enter the path of the S3 bucket (prefix it with `s3://`). + 2. Navigate to **Browse S3**, select the S3 bucket you created, and click **Choose**. +1. **Save** these settings. +1. In the **query editor**, create a database by running `create database YOUR_DATABASE_NAME`. +1. To make the database you created the one you `write` into, select it from the **Database** list on the left side menu. +1. Access the Jaffle Shop data in the S3 bucket using one of these options: + 1. Manually create the tables. + 2. Create a glue crawler to recreate the data as external tables (recommended). +1. Once the tables have been created, you will able to `SELECT` from them. + +## Set up security access to Athena + +To setup the security access for Athena, determine which access method you want to use: +* Obtain `aws_access_key_id` and `aws_secret_access_key` (recommended) +* Obtain an **AWS credentials** file. + +### AWS access key (recommended) + +To obtain your `aws_access_key_id` and `aws_secret_access_key`: + +1. Open the **AWS Console**. +1. Click on your **username** near the top right and click **Security Credentials**. +1. Click on **Users** in the sidebar. +1. Click on your **username** (or the name of the user for whom to create the key). +1. Click on the **Security Credentials** tab. +1. Click **Create Access Key**. +1. Click **Show User Security Credentials** and + +Save the `aws_access_key_id` and `aws_secret_access_key` for a future step. + +### AWS credentials file + +To obtain your AWS credentials file: +1. Follow the instructions for [configuring the credentials file](https://docs.aws.amazon.com/cli/v1/userguide/cli-configure-files.html) usin the AWS CLI +1. Locate the `~/.aws/credentials` file on your computer + 1. Windows: `%USERPROFILE%\.aws\credentials` + 2. Mac/Linux: `~/.aws/credentials` + +Retrieve the `aws_access_key_id` and `aws_secret_access_key` from the `~/.aws/credentials` file for a future step. + +## Configure the connection in dbt Cloud + +To configure the Athena connection in dbt Cloud: +1. Click your **account name** on the left-side menu and click **Account settings**. +1. Click **Connections** and click **New connection**. +1. Click **Athena** and fill out the required fields (and any optional fields). + 1. **AWS region name** — The AWS region of your environment. + 1. **Database (catalog)** — Enter the database name created in earlier steps (lowercase only). + 1. **AWS S3 staging directory** — Enter the S3 bucket created in earlier steps. +1. Click **Save** + +### Configure your environment + +To configure the Athena credentials in your environment: +1. Click **Deploy** on the left-side menu and click **Environments**. +1. Click **Create environment** and fill out the **General settings**. + - Your **dbt version** must be set to `Versionless` to use the Athena connection. +1. Select the Athena connection from the **Connection** dropdown. +1. Fill out the `aws_access_key` and `aws_access_id` recorded in previous steps, as well as the `Schema` to write to. +1. Click **Test connection** and once it succeeds, **Save** the environment. + +Repeat the process to create a [development environment](https://docs.getdbt.com/docs/dbt-cloud-environments#types-of-environments). + +## Set up a dbt Cloud managed repository + + +## Initialize your dbt project​ and start developing + +Now that you have a repository configured, you can initialize your project and start development in dbt Cloud: + +1. Click **Start developing in the IDE**. It might take a few minutes for your project to spin up for the first time as it establishes your git connection, clones your repo, and tests the connection to the warehouse. +2. Above the file tree to the left, click **Initialize dbt project**. This builds out your folder structure with example models. +3. Make your initial commit by clicking **Commit and sync**. Use the commit message `initial commit` and click **Commit**. This creates the first commit to your managed repo and allows you to open a branch where you can add new dbt code. +4. You can now directly query data from your warehouse and execute `dbt run`. You can try this out now: + - Click **+ Create new file**, add this query to the new file, and click **Save as** to save the new file: + ```sql + select * from jaffle_shop.customers + ``` + - In the command line bar at the bottom, enter `dbt run` and click **Enter**. You should see a `dbt run succeeded` message. + +## Build your first model + +You have two options for working with files in the dbt Cloud IDE: + +- Create a new branch (recommended) — Create a new branch to edit and commit your changes. Navigate to **Version Control** on the left sidebar and click **Create branch**. +- Edit in the protected primary branch — If you prefer to edit, format, or lint files and execute dbt commands directly in your primary git branch. The dbt Cloud IDE prevents commits to the protected branch, so you will be prompted to commit your changes to a new branch. + +Name the new branch `add-customers-model`. + +1. Click the **...** next to the `models` directory, then select **Create file**. +2. Name the file `customers.sql`, then click **Create**. +3. Copy the following query into the file and click **Save**. + +```sql +with customers as ( + + select + id as customer_id, + first_name, + last_name + + from jaffle_shop.customers + +), + +orders as ( + + select + id as order_id, + user_id as customer_id, + order_date, + status + + from jaffle_shop.orders + +), + +customer_orders as ( + + select + customer_id, + + min(order_date) as first_order_date, + max(order_date) as most_recent_order_date, + count(order_id) as number_of_orders + + from orders + + group by 1 + +), + +final as ( + + select + customers.customer_id, + customers.first_name, + customers.last_name, + customer_orders.first_order_date, + customer_orders.most_recent_order_date, + coalesce(customer_orders.number_of_orders, 0) as number_of_orders + + from customers + + left join customer_orders using (customer_id) + +) + +select * from final +``` + +4. Enter `dbt run` in the command prompt at the bottom of the screen. You should get a successful run and see the three models. + +Later, you can connect your business intelligence (BI) tools to these views and tables so they only read cleaned up data rather than raw data in your BI tool. + +#### FAQs + + + + + + + +## Change the way your model is materialized + + + +## Delete the example models + + + +## Build models on top of other models + + + +1. Create a new SQL file, `models/stg_customers.sql`, with the SQL from the `customers` CTE in our original query. +2. Create a second new SQL file, `models/stg_orders.sql`, with the SQL from the `orders` CTE in our original query. + + + + ```sql + select + id as customer_id, + first_name, + last_name + + from jaffle_shop.customers + ``` + + + + + + ```sql + select + id as order_id, + user_id as customer_id, + order_date, + status + + from jaffle_shop.orders + ``` + + + +3. Edit the SQL in your `models/customers.sql` file as follows: + + + + ```sql + with customers as ( + + select * from {{ ref('stg_customers') }} + + ), + + orders as ( + + select * from {{ ref('stg_orders') }} + + ), + + customer_orders as ( + + select + customer_id, + + min(order_date) as first_order_date, + max(order_date) as most_recent_order_date, + count(order_id) as number_of_orders + + from orders + + group by 1 + + ), + + final as ( + + select + customers.customer_id, + customers.first_name, + customers.last_name, + customer_orders.first_order_date, + customer_orders.most_recent_order_date, + coalesce(customer_orders.number_of_orders, 0) as number_of_orders + + from customers + + left join customer_orders using (customer_id) + + ) + + select * from final + + ``` + + + +4. Execute `dbt run`. + + This time, when you performed a `dbt run`, separate views/tables were created for `stg_customers`, `stg_orders` and `customers`. dbt inferred the order to run these models. Because `customers` depends on `stg_customers` and `stg_orders`, dbt builds `customers` last. You do not need to explicitly define these dependencies. + + +#### FAQs {#faq-2} + + + + + +
+ + + + diff --git a/website/docs/guides/azure-synapse-analytics-qs.md b/website/docs/guides/azure-synapse-analytics-qs.md index 4f0285e6623..94beddfec80 100644 --- a/website/docs/guides/azure-synapse-analytics-qs.md +++ b/website/docs/guides/azure-synapse-analytics-qs.md @@ -92,7 +92,7 @@ In this quickstart guide, you'll learn how to use dbt Cloud with [Azure Synapse ## Connect dbt Cloud to Azure Synapse Analytics -1. Create a new project in dbt Cloud. Open the gear menu in the top right corner, select **Account settings** and click **+ New Project**. +1. Create a new project in dbt Cloud. Click on your account name in the left side menu, select **Account settings**, and click **+ New Project**. 2. Enter a project name and click **Continue**. 3. Choose **Synapse** as your connection and click **Next**. 4. In the **Configure your environment** section, enter the **Settings** for your new project: diff --git a/website/docs/guides/bigquery-qs.md b/website/docs/guides/bigquery-qs.md index 19a4ff8fbb0..194b73f25bf 100644 --- a/website/docs/guides/bigquery-qs.md +++ b/website/docs/guides/bigquery-qs.md @@ -85,13 +85,14 @@ In order to let dbt connect to your warehouse, you'll need to generate a keyfile 3. Create a service account key for your new project from the [Service accounts page](https://console.cloud.google.com/iam-admin/serviceaccounts?walkthrough_id=iam--create-service-account-keys&start_index=1#step_index=1). For more information, refer to [Create a service account key](https://cloud.google.com/iam/docs/creating-managing-service-account-keys#creating) in the Google Cloud docs. When downloading the JSON file, make sure to use a filename you can easily remember. For example, `dbt-user-creds.json`. For security reasons, dbt Labs recommends that you protect this JSON file like you would your identity credentials; for example, don't check the JSON file into your version control software. ## Connect dbt Cloud to BigQuery​ -1. Create a new project in [dbt Cloud](/docs/cloud/about-cloud/access-regions-ip-addresses). From **Account settings** (using the gear menu in the top right corner), click **+ New Project**. +1. Create a new project in [dbt Cloud](/docs/cloud/about-cloud/access-regions-ip-addresses). Navigate to **Account settings** (by clicking on your account name in the left side menu), and click **+ New project**. 2. Enter a project name and click **Continue**. 3. For the warehouse, click **BigQuery** then **Next** to set up your connection. 4. Click **Upload a Service Account JSON File** in settings. 5. Select the JSON file you downloaded in [Generate BigQuery credentials](#generate-bigquery-credentials) and dbt Cloud will fill in all the necessary fields. -6. Click **Test Connection**. This verifies that dbt Cloud can access your BigQuery account. -7. Click **Next** if the test succeeded. If it failed, you might need to go back and regenerate your BigQuery credentials. +6. Optional — dbt Cloud Enterprise plans can configure developer OAuth with BigQuery, providing an additional layer of security. For more information, refer to [Set up BigQuery OAuth](/docs/cloud/manage-access/set-up-bigquery-oauth). +7. Click **Test Connection**. This verifies that dbt Cloud can access your BigQuery account. +8. Click **Next** if the test succeeded. If it failed, you might need to go back and regenerate your BigQuery credentials. ## Set up a dbt Cloud managed repository diff --git a/website/docs/guides/core-cloud-2.md b/website/docs/guides/core-cloud-2.md index cee1e8029c2..ddc0e883d84 100644 --- a/website/docs/guides/core-cloud-2.md +++ b/website/docs/guides/core-cloud-2.md @@ -155,7 +155,7 @@ After [setting the foundations of dbt Cloud](https://docs.getdbt.com/guides/core Once you’ve confirmed that dbt Cloud orchestration and CI/CD are working as expected, you should pause your current orchestration tool and stop or update your current CI/CD process. This is not relevant if you’re still using an external orchestrator (such as Airflow), and you’ve swapped out `dbt-core` execution for dbt Cloud execution (through the [API](/docs/dbt-cloud-apis/overview)). Familiarize your team with dbt Cloud's [features](/docs/cloud/about-cloud/dbt-cloud-features) and optimize development and deployment processes. Some key features to consider include: -- **Version management:** Manage [dbt versions](/docs/dbt-versions/upgrade-dbt-version-in-cloud) and ensure team collaboration with dbt Cloud's one-click feature, removing the hassle of manual updates and version discrepancies. You can go [**Versionless**](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) to always get the latest features and early access to new functionality for your dbt project. +- **Release tracks:** Choose a [release track](/docs/dbt-versions/cloud-release-tracks) for automatic dbt version upgrades, at the cadence appropriate for your team — removing the hassle of manual updates and the risk of version discrepancies. You can also get early access to new functionality, ahead of dbt Core. - **Development tools**: Use the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation) or [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) to build, test, run, and version control your dbt projects. - **Documentation and Source freshness:** Automate storage of [documentation](/docs/build/documentation) and track [source freshness](/docs/deploy/source-freshness) in dbt Cloud, which streamlines project maintenance. - **Notifications and logs:** Receive immediate [notifications](/docs/deploy/monitor-jobs) for job failures, with direct links to the job details. Access comprehensive logs for all job runs to help with troubleshooting. diff --git a/website/docs/guides/core-to-cloud-1.md b/website/docs/guides/core-to-cloud-1.md index efed66c862a..3d6b119c178 100644 --- a/website/docs/guides/core-to-cloud-1.md +++ b/website/docs/guides/core-to-cloud-1.md @@ -58,8 +58,7 @@ This guide outlines the steps you need to take to move from dbt Core to dbt Clou ## Prerequisites -- You have an existing dbt Core project connected to a Git repository and data platform supported in [dbt Cloud](/docs/cloud/connect-data-platform/about-connections). -- A [supported version](/docs/dbt-versions/core) of dbt or select [**Versionless**](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) of dbt. +- You have an existing dbt Core project connected to a Git repository and data platform supported in [dbt Cloud](/docs/cloud/connect-data-platform/about-connections). - You have a dbt Cloud account. **[Don't have one? Start your free trial today](https://www.getdbt.com/signup)**! ## Account setup @@ -147,8 +146,8 @@ The most common data environments are production, staging, and development. The ### Initial setup steps 1. **Set up development environment** — Set up your [development](/docs/dbt-cloud-environments#create-a-development-environment) environment and [development credentials](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud#access-the-cloud-ide). You’ll need this to access your dbt project and start developing. -2. **dbt Core version** — In your dbt Cloud environment and credentials, use the same dbt Core version you use locally. You can run `dbt --version` in the command line to find out which version of dbt Core you’re using. - - When using dbt Core, you need to think about which version you’re using and manage your own upgrades. When using dbt Cloud, leverage ["Versionless"](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) so you don’t have to. +2. **dbt Core version** — In your dbt Cloud environment, select a [release track](/docs/dbt-versions/cloud-release-tracks) for ongoing dbt version upgrades. If your team plans to use both dbt Core and dbt Cloud for developing or deploying your dbt project, You can run `dbt --version` in the command line to find out which version of dbt Core you’re using. + - When using dbt Core, you need to think about which version you’re using and manage your own upgrades. When using dbt Cloud, leverage [release tracks](/docs/dbt-versions/cloud-release-tracks) so you don’t have to. 3. **Connect to your data platform** — When using dbt Cloud, you can [connect to your data platform](/docs/cloud/connect-data-platform/about-connections) directly in the UI. - Each environment is roughly equivalent to an entry in your `profiles.yml` file. This means you don't need a `profiles.yml` file in your project. @@ -210,7 +209,7 @@ To use the [dbt Cloud's job scheduler](/docs/deploy/job-scheduler), set up one e ### Initial setup steps 1. **dbt Core version** — In your environment settings, configure dbt Cloud with the same dbt Core version. - - Once your full migration is complete, we recommend upgrading your environments to ["Versionless"](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) to always get the latest features and more. You only need to do this once. + - Once your full migration is complete, we recommend upgrading your environments to [release tracks](/docs/dbt-versions/cloud-release-tracks) to always get the latest features and more. You only need to do this once. 2. **Configure your jobs** — [Create jobs](/docs/deploy/deploy-jobs#create-and-schedule-jobs) for scheduled or event-driven dbt jobs. You can use cron execution, manual, pull requests, or trigger on the completion of another job. - Note that alongside [jobs in dbt Cloud](/docs/deploy/jobs), discover other ways to schedule and run your dbt jobs with the help of other tools. Refer to [Integrate with other tools](/docs/deploy/deployment-tools) for more information. diff --git a/website/docs/guides/core-to-cloud-3.md b/website/docs/guides/core-to-cloud-3.md index 7d482d54471..81222471345 100644 --- a/website/docs/guides/core-to-cloud-3.md +++ b/website/docs/guides/core-to-cloud-3.md @@ -36,7 +36,7 @@ You may have already started your move to dbt Cloud and are looking for tips to In dbt Cloud, you can natively connect to your data platform and test its [connection](/docs/connect-adapters) with a click of a button. This is especially useful for users who are new to dbt Cloud or are looking to streamline their connection setup. Here are some tips and caveats to consider: ### Tips -- Manage [dbt versions](/docs/dbt-versions/upgrade-dbt-version-in-cloud) and ensure team collaboration with dbt Cloud's one-click feature, eliminating the need for manual updates and version discrepancies. You can go [**Versionless**](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) to always get the latest features and early access to new functionality for your dbt project. +- Manage [dbt versions](/docs/dbt-versions/upgrade-dbt-version-in-cloud) and ensure team collaboration with dbt Cloud's one-click feature, eliminating the need for manual updates and version discrepancies. Select a [release track](/docs/dbt-versions/cloud-release-tracks) for ongoing updates, to always stay up to date with fixes and (optionally) get early access to new functionality for your dbt project. - dbt Cloud supports a whole host of [cloud providers](/docs/cloud/connect-data-platform/about-connections), including Snowflake, Databricks, BigQuery, Fabric, and Redshift (to name a few). - Use [Extended Attributes](/docs/deploy/deploy-environments#extended-attributes) to set a flexible [profiles.yml](/docs/core/connect-data-platform/profiles.yml) snippet in your dbt Cloud environment settings. It gives you more control over environments (both deployment and development) and extends how dbt Cloud connects to the data platform within a given environment. - For example, if you have a field in your `profiles.yml` that you’d like to add to the dbt Cloud adapter user interface, you can use Extended Attributes to set it. diff --git a/website/docs/guides/custom-cicd-pipelines.md b/website/docs/guides/custom-cicd-pipelines.md index be23524d096..668d3f6f1dd 100644 --- a/website/docs/guides/custom-cicd-pipelines.md +++ b/website/docs/guides/custom-cicd-pipelines.md @@ -506,7 +506,7 @@ Additionally, you’ll see the job in the run history of dbt Cloud. It should be - + diff --git a/website/docs/guides/dbt-python-snowpark.md b/website/docs/guides/dbt-python-snowpark.md index 2e74c9722d8..091f1006992 100644 --- a/website/docs/guides/dbt-python-snowpark.md +++ b/website/docs/guides/dbt-python-snowpark.md @@ -286,7 +286,7 @@ We need to obtain our data source by copying our Formula 1 data into Snowflake t ## Change development schema name navigate the IDE -1. First we are going to change the name of our default schema to where our dbt models will build. By default, the name is `dbt_`. We will change this to `dbt_` to create your own personal development schema. To do this, select **Profile Settings** from the gear icon in the upper right. +1. First we are going to change the name of our default schema to where our dbt models will build. By default, the name is `dbt_`. We will change this to `dbt_` to create your own personal development schema. To do this, click on your account name in the left side menu and select **Account settings**. diff --git a/website/docs/guides/manual-install-qs.md b/website/docs/guides/manual-install-qs.md index 2e10cdac07c..816a9bd07ee 100644 --- a/website/docs/guides/manual-install-qs.md +++ b/website/docs/guides/manual-install-qs.md @@ -36,7 +36,7 @@ The following steps use [GitHub](https://github.com/) as the Git provider for th 2. Select **Public** so the repository can be shared with others. You can always make it private later. 3. Leave the default values for all other settings. 4. Click **Create repository**. -5. Save the commands from "…or create a new repository on the command line" to use later in [Commit your changes](#commit-your-changes). +5. Save the commands from "…or create a new repository on the command line" to use later in [Commit your changes](https://docs.getdbt.com/guides/manual-install?step=6). ## Create a project @@ -162,7 +162,7 @@ You should have an output that looks like this: Commit your changes so that the repository contains the latest code. -1. Link the GitHub repository you created to your dbt project by running the following commands in Terminal. Make sure you use the correct git URL for your repository, which you should have saved from step 5 in [Create a repository](#create-a-repository). +1. Link the GitHub repository you created to your dbt project by running the following commands in Terminal. Make sure you use the correct git URL for your repository, which you should have saved from step 5 in [Create a repository](https://docs.getdbt.com/guides/manual-install?step=2). ```shell git init diff --git a/website/docs/guides/mesh-qs.md b/website/docs/guides/mesh-qs.md index 47ece7b29ec..9a7aa8b0ce0 100644 --- a/website/docs/guides/mesh-qs.md +++ b/website/docs/guides/mesh-qs.md @@ -40,7 +40,6 @@ To leverage dbt Mesh, you need the following: - You must have a [dbt Cloud Enterprise account](https://www.getdbt.com/get-started/enterprise-contact-pricing) - You have access to a cloud data platform, permissions to load the sample data tables, and dbt Cloud permissions to create new projects. -- Set your development and deployment [environments](/docs/dbt-cloud-environments) to use dbt [version](/docs/dbt-versions/core) 1.6 or later. You can also opt to go ["Versionless"](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) to always get the most recent features and functionality. - This guide uses the Jaffle Shop sample data, including `customers`, `orders`, and `payments` tables. Follow the provided instructions to load this data into your respective data platform: - [Snowflake](https://docs.getdbt.com/guides/snowflake?step=3) - [Databricks](https://docs.getdbt.com/guides/databricks?step=3) diff --git a/website/docs/guides/microsoft-fabric-qs.md b/website/docs/guides/microsoft-fabric-qs.md index 157ab2e6b89..6bacf4177df 100644 --- a/website/docs/guides/microsoft-fabric-qs.md +++ b/website/docs/guides/microsoft-fabric-qs.md @@ -101,7 +101,7 @@ In this quickstart guide, you'll learn how to use dbt Cloud with [Microsoft Fabr ## Connect dbt Cloud to Microsoft Fabric -1. Create a new project in dbt Cloud. From **Account settings** (using the gear menu in the top right corner), click **+ New Project**. +1. Create a new project in dbt Cloud. Navigate to **Account settings** (by clicking on your account name in the left side menu), and click **+ New Project**. 2. Enter a project name and click **Continue**. 3. Choose **Fabric** as your connection and click **Next**. 4. In the **Configure your environment** section, enter the **Settings** for your new project: diff --git a/website/docs/guides/redshift-qs.md b/website/docs/guides/redshift-qs.md index 8b950472506..83fafad1d12 100644 --- a/website/docs/guides/redshift-qs.md +++ b/website/docs/guides/redshift-qs.md @@ -170,7 +170,7 @@ Now we are going to load our sample data into the S3 bucket that our Cloudformat ``` ## Connect dbt Cloud to Redshift -1. Create a new project in [dbt Cloud](/docs/cloud/about-cloud/access-regions-ip-addresses). From **Account settings** (using the gear menu in the top right corner), click **+ New Project**. +1. Create a new project in [dbt Cloud](/docs/cloud/about-cloud/access-regions-ip-addresses). Navigate to **Account settings** (by clicking on your account name in the left side menu), and click **+ New Project**. 2. Enter a project name and click **Continue**. 3. For the warehouse, click **Redshift** then **Next** to set up your connection. 4. Enter your Redshift settings. Reference your credentials you saved from the CloudFormation template. diff --git a/website/docs/guides/sl-snowflake-qs.md b/website/docs/guides/sl-snowflake-qs.md index b5a0e559c5b..79038cd1dfc 100644 --- a/website/docs/guides/sl-snowflake-qs.md +++ b/website/docs/guides/sl-snowflake-qs.md @@ -106,7 +106,6 @@ Open a new tab and follow these quick steps for account setup and data loading i -- Production and development environments must be on [dbt version 1.6 or higher](/docs/dbt-versions/upgrade-dbt-version-in-cloud). Alternatively, set your environment to [**Versionless**](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) to always get the latest updates. - Create a [trial Snowflake account](https://signup.snowflake.com/): - Select the Enterprise Snowflake edition with ACCOUNTADMIN access. Consider organizational questions when choosing a cloud provider, refer to Snowflake's [Introduction to Cloud Platforms](https://docs.snowflake.com/en/user-guide/intro-cloud-platforms). - Select a cloud provider and region. All cloud providers and regions will work so choose whichever you prefer. @@ -291,7 +290,7 @@ Using Partner Connect allows you to create a complete dbt account with your [Sno 5. After you have filled out the form and clicked **Complete Registration**, you will be logged into dbt Cloud automatically. -6. From your **Account Settings** in dbt Cloud (using the gear menu in the upper right corner), choose the "Partner Connect Trial" project and select **snowflake** in the overview table. Select **Edit** and update the **Database** field to `analytics` and the **Warehouse** field to `transforming`. +6. Click your account name in the left side menu and select **Account settings**, choose the "Partner Connect Trial" project, and select **snowflake** in the overview table. Select **Edit** and update the **Database** field to `analytics` and the **Warehouse** field to `transforming`. @@ -301,7 +300,7 @@ Using Partner Connect allows you to create a complete dbt account with your [Sno -1. Create a new project in dbt Cloud. From **Account settings** (using the gear menu in the top right corner), click **+ New Project**. +1. Create a new project in dbt Cloud. Navigate to **Account settings** (by clicking on your account name in the left side menu), and click **+ New Project**. 2. Enter a project name and click **Continue**. 3. For the warehouse, click **Snowflake** then **Next** to set up your connection. diff --git a/website/docs/guides/snowflake-qs.md b/website/docs/guides/snowflake-qs.md index bc27d1e1a4f..f1edd5ffc00 100644 --- a/website/docs/guides/snowflake-qs.md +++ b/website/docs/guides/snowflake-qs.md @@ -170,7 +170,7 @@ Using Partner Connect allows you to create a complete dbt account with your [Sno 5. After you have filled out the form and clicked **Complete Registration**, you will be logged into dbt Cloud automatically. -6. From your **Account Settings** in dbt Cloud (using the gear menu in the upper right corner), choose the "Partner Connect Trial" project and select **snowflake** in the overview table. Select edit and update the fields **Database** and **Warehouse** to be `analytics` and `transforming`, respectively. +6. Go to the left side menu and click your account name, then select **Account settings**, choose the "Partner Connect Trial" project, and select **snowflake** in the overview table. Select edit and update the fields **Database** and **Warehouse** to be `analytics` and `transforming`, respectively. @@ -180,7 +180,7 @@ Using Partner Connect allows you to create a complete dbt account with your [Sno -1. Create a new project in dbt Cloud. From **Account settings** (using the gear menu in the top right corner), click **+ New Project**. +1. Create a new project in dbt Cloud. Navigate to **Account settings** (by clicking on your account name in the left side menu), and click **+ New Project**. 2. Enter a project name and click **Continue**. 3. For the warehouse, click **Snowflake** then **Next** to set up your connection. @@ -230,6 +230,26 @@ Now that you have a repository configured, you can initialize your project and s ``` - In the command line bar at the bottom, enter `dbt run` and click **Enter**. You should see a `dbt run succeeded` message. +:::info +If you receive an insufficient privileges error on Snowflake at this point, it may be because your Snowflake role doesn't have permission to access the raw source data, to build target tables and views, or both. + +To troubleshoot, use a role with sufficient privileges (like `ACCOUNTADMIN`) and run the following commands in Snowflake. + +**Note**: Replace `snowflake_role_name` with the role you intend to use. If you launched dbt Cloud with Snowflake Partner Connect, use `pc_dbt_role` as the role. + +``` +grant all on database raw to role snowflake_role_name; +grant all on database analytics to role snowflake_role_name; + +grant all on schema raw.jaffle_shop to role snowflake_role_name; +grant all on schema raw.stripe to role snowflake_role_name; + +grant all on all tables in database raw to role snowflake_role_name; +grant all on future tables in database raw to role snowflake_role_name; +``` + +::: + ## Build your first model You have two options for working with files in the dbt Cloud IDE: diff --git a/website/docs/guides/starburst-galaxy-qs.md b/website/docs/guides/starburst-galaxy-qs.md index 316e392483d..76e4abadd64 100644 --- a/website/docs/guides/starburst-galaxy-qs.md +++ b/website/docs/guides/starburst-galaxy-qs.md @@ -203,7 +203,7 @@ To query the Jaffle Shop data with Starburst Galaxy, you need to create tables u 3. Click **Clusters** on the left sidebar. 4. Find your cluster in the **View clusters** table and click **Connection info**. Choose **dbt** from the **Select client** dropdown. Keep the **Connection information** modal open. You will use details from that modal in dbt Cloud. 5. In another browser tab, log in to [dbt Cloud](/docs/cloud/about-cloud/access-regions-ip-addresses). -6. Create a new project in dbt Cloud. From Account settings (using the gear menu in the top right corner), click **+ New Project**. +6. Create a new project in dbt Cloud. Click on your account name in the left side menu, select **Account settings**, and click **+ New Project**. 7. Enter a project name and click **Continue**. 8. Choose **Starburst** as your connection and click **Next**. 9. Enter the **Settings** for your new project: diff --git a/website/docs/guides/teradata-qs.md b/website/docs/guides/teradata-qs.md index d662f3bce3f..338997f274a 100644 --- a/website/docs/guides/teradata-qs.md +++ b/website/docs/guides/teradata-qs.md @@ -104,7 +104,7 @@ If you created your Teradata Vantage database instance at https://clearscape.ter ## Connect dbt Cloud to Teradata -1. Create a new project in dbt Cloud. From **Account settings** (using the gear menu in the top right corner), click **New Project**. +1. Create a new project in dbt Cloud. Click on your account name in the left side menu, select **Account settings**, and click **+ New Project**. 2. Enter a project name and click **Continue**. 3. In **Configure your development environment**, click **Add new connection**. 4. Select **Teradata**, fill in all the required details in the **Settings** section, and test the connection. diff --git a/website/docs/guides/zapier-ms-teams.md b/website/docs/guides/zapier-ms-teams.md index 500ae4bf9e5..e52205d315f 100644 --- a/website/docs/guides/zapier-ms-teams.md +++ b/website/docs/guides/zapier-ms-teams.md @@ -136,7 +136,7 @@ for step in run_data_results['run_steps']: # Remove timestamp and any colour tags full_log = re.sub('\x1b?\[[0-9]+m[0-9:]*', '', full_log) - summary_start = re.search('(?:Completed with \d+ errors? and \d+ warnings?:|Database Error|Compilation Error|Runtime Error)', full_log) + summary_start = re.search('(?:Completed with \d+ error.* and \d+ warnings?:|Database Error|Compilation Error|Runtime Error)', full_log) line_items = re.findall('(^.*(?:Failure|Error) in .*\n.*\n.*)', full_log, re.MULTILINE) diff --git a/website/docs/guides/zapier-slack.md b/website/docs/guides/zapier-slack.md index e11da493b67..99c8347424a 100644 --- a/website/docs/guides/zapier-slack.md +++ b/website/docs/guides/zapier-slack.md @@ -134,7 +134,7 @@ for step in run_data_results['run_steps']: # Remove timestamp and any colour tags full_log = re.sub('\x1b?\[[0-9]+m[0-9:]*', '', full_log) - summary_start = re.search('(?:Completed with \d+ errors? and \d+ warnings?:|Database Error|Compilation Error|Runtime Error)', full_log) + summary_start = re.search('(?:Completed with \d+ error.* and \d+ warnings?:|Database Error|Compilation Error|Runtime Error)', full_log) line_items = re.findall('(^.*(?:Failure|Error) in .*\n.*\n.*)', full_log, re.MULTILINE) @@ -277,7 +277,7 @@ for step in results['run_steps']: # Remove timestamp and any colour tags full_log = re.sub('\x1b?\[[0-9]+m[0-9:]*', '', full_log) - summary_start = re.search('(?:Completed with \d+ errors? and \d+ warnings?:|Database Error|Compilation Error|Runtime Error)', full_log) + summary_start = re.search('(?:Completed with \d+ error.* and \d+ warnings?:|Database Error|Compilation Error|Runtime Error)', full_log) line_items = re.findall('(^.*(?:Failure|Error) in .*\n.*\n.*)', full_log, re.MULTILINE) if not summary_start: diff --git a/website/docs/reference/commands/deps.md b/website/docs/reference/commands/deps.md index 0cb8e50f7a6..6755dbbcb3c 100644 --- a/website/docs/reference/commands/deps.md +++ b/website/docs/reference/commands/deps.md @@ -60,25 +60,47 @@ Update your versions in packages.yml, then run dbt deps ## Predictable package installs -Starting in dbt Core v1.7, dbt generates a `package-lock.yml` file in the root of your project. This contains the complete set of resolved packages based on the `packages` configuration in `dependencies.yml` or `packages.yml`. Each subsequent invocation of `dbt deps` will install from the _locked_ set of packages specified in this file. Storing the complete set of required packages (with pinned versions) in version-controlled code ensures predictable installs in production and consistency across all developers and environments. +Starting in dbt v1.7, dbt generates a `package-lock.yml` file in the root of your project. This file ensures consistent and predictable package installs by storing the exact versions (including commit SHAs) of all resolved packages specified in your `packages.yml` or `dependencies.yml`. This consistency is crucial for maintaining stability in development and production environments, preventing unexpected issues from new releases with potential bugs. -The `package-lock.yml` file should be committed in Git initially, and then updated and committed only when you want to change versions or uninstall a package (for example `dbt deps --upgrade` or `dbt deps --lock`). +When you run `dbt deps`, dbt installs packages based on the locked versions in `package-lock.yml`. To update these locked versions, you must explicitly run `dbt deps --upgrade` and commit the updated `package-lock.yml` file. Storing this file in version control guarantees consistency across all environments and for all developers. -The `package-lock.yml` file includes a `sha1_hash` of the `packages` config. This enables dbt to detect if the `packages` config has been updated, and to rerun dependency resolution. To only check for changes to the `packages` config and update the lock file accordingly without installing those packages, provide the `--lock` flag (that is, `dbt deps --lock`). +### Managing `package-lock.yml` -### Forcing upgrades +The `package-lock.yml` file should be committed to Git initially and updated only when you intend to change versions or uninstall a package. For example, run `dbt deps --upgrade` to get updated package versions or `dbt deps --lock` to update the lock file based on changes to the packages config without installing the packages. -It's possible to force package resolution to rerun, even if the `packages` config hasn't changed, by running `dbt deps --upgrade`. This enables you to get the latest commits from the `main` branch of an internally maintained `git` package while accepting the risk of unpredictable builds. +To bypass using `package-lock.yml` entirely, you can add it to your project's `.gitignore`. However, this approach sacrifices the predictability of builds. If you choose this route, we strongly recommend adding version pins for third-party packages in your `packages` config. -An alternative to running `dbt deps --upgrade` in production is to "ignore" the lock file by adding `package-lock.yml` to your project's `.gitignore` file. +### Detecting changes in `packages` config -If you pursue either approach, dbt Labs strongly recommends adding version pins for third-party packages within your `packages` config. +The `package-lock.yml` file includes a `sha1_hash` of your packages config. If you update `packages.yml`, dbt will detect the change and rerun dependency resolution during the next `dbt deps` command. To update the lock file without installing the new packages, use the `--lock` flag: -## Add specific packages +```shell +dbt deps --lock +``` + +### Forcing package updates + +To update all packages, even if `packages.yml` hasn’t changed, use the `--upgrade` flag: + +```shell + +dbt deps --upgrade + +``` + +This is particularly useful for fetching the latest commits from the `main` branch of an internally maintained Git package. + +:::warning +Forcing package upgrades may introduce build inconsistencies unless carefully managed. +::: + +### Adding specific packages -The `dbt deps` command can add or update an existing package configuration — no need to remember the exact syntax for package configurations. +The `dbt deps` command can add or update package configurations directly, saving you from remembering exact syntax. -For Hub packages (default), which are the easiest to install: +#### Hub packages (default) + +Hub packages are the default package types and the easiest to install. ```shell dbt deps --add-package dbt-labs/dbt_utils@1.0.0 @@ -87,11 +109,15 @@ dbt deps --add-package dbt-labs/dbt_utils@1.0.0 dbt deps --add-package dbt-labs/snowplow@">=0.7.0,<0.8.0" ``` -For other package types, use the `--source` flag: +#### Non-Hub packages + +Use the `--source` flag to specify the type of package to be installed: + ```shell -# add package from git + +# Git package dbt deps --add-package https://github.com/fivetran/dbt_amplitude@v0.3.0 --source git -# add package from local +# Local package dbt deps --add-package /opt/dbt/redshift --source local ``` diff --git a/website/docs/reference/commands/init.md b/website/docs/reference/commands/init.md index 112fff63a38..7b71bf70f45 100644 --- a/website/docs/reference/commands/init.md +++ b/website/docs/reference/commands/init.md @@ -31,7 +31,7 @@ If you've just cloned or downloaded an existing dbt project, `dbt init` can stil `dbt init` knows how to prompt for connection information by looking for a file named `profile_template.yml`. It will look for this file in two places: -- **Adapter plugin:** What's the bare minumum Postgres profile? What's the type of each field, what are its defaults? This information is stored in a file called [`dbt/include/postgres/profile_template.yml`](https://github.com/dbt-labs/dbt-core/blob/main/plugins/postgres/dbt/include/postgres/profile_template.yml). If you're the maintainer of an adapter plugin, we highly recommend that you add a `profile_template.yml` to your plugin, too. Refer to the [Build, test, document, and promote adapters](/guides/adapter-creation) guide for more information. +- **Adapter plugin:** What's the bare minumum Postgres profile? What's the type of each field, what are its defaults? This information is stored in a file called [`dbt/include/postgres/profile_template.yml`](https://github.com/dbt-labs/dbt-postgres/blob/main/dbt/include/postgres/profile_template.yml). If you're the maintainer of an adapter plugin, we highly recommend that you add a `profile_template.yml` to your plugin, too. Refer to the [Build, test, document, and promote adapters](/guides/adapter-creation) guide for more information. - **Existing project:** If you're the maintainer of an existing project, and you want to help new users get connected to your database quickly and easily, you can include your own custom `profile_template.yml` in the root of your project, alongside `dbt_project.yml`. For common connection attributes, set the values in `fixed`; leave user-specific attributes in `prompts`, but with custom hints and defaults as you'd like. diff --git a/website/docs/reference/commands/run.md b/website/docs/reference/commands/run.md index 26db40cb7e4..58a876f98ef 100644 --- a/website/docs/reference/commands/run.md +++ b/website/docs/reference/commands/run.md @@ -83,4 +83,15 @@ See [global configs](/reference/global-configs/print-output#print-color) The `run` command supports the `--empty` flag for building schema-only dry runs. The `--empty` flag limits the refs and sources to zero rows. dbt will still execute the model SQL against the target data warehouse but will avoid expensive reads of input data. This validates dependencies and ensures your models will build properly. - \ No newline at end of file + + +## Status codes + +When calling the [list_runs api](/dbt-cloud/api-v2#/operations/List%20Runs), you will get a status code for each run returned. The available run status codes are as follows: + +- Starting = 1 +- Running = 3 +- Success = 10 +- Error = 20 +- Canceled = 30 +- Skipped = 40 diff --git a/website/docs/reference/commands/version.md b/website/docs/reference/commands/version.md index 2ed14117828..4d5ce6524dd 100644 --- a/website/docs/reference/commands/version.md +++ b/website/docs/reference/commands/version.md @@ -13,7 +13,7 @@ The `--version` command-line flag returns information about the currently instal ## Versioning To learn more about release versioning for dbt Core, refer to [How dbt Core uses semantic versioning](/docs/dbt-versions/core#how-dbt-core-uses-semantic-versioning). -If using [versionless dbt Cloud](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless), then `dbt_version` uses the latest (continuous) release version. This also follows semantic versioning guidelines, using the `YYYY.xx.yy` format, where the year is the major version (for example, `2024.04.1234`) +If using a [dbt Cloud release track](/docs/dbt-versions/cloud-release-tracks), which provide ongoing updates to dbt, then `dbt_version` represents the release version of dbt in dbt Cloud. This also follows semantic versioning guidelines, using the `YYYY.MM.DD+` format. The year, month, and day represent the date the version was built (for example, `2024.10.28+996c6a8`). The suffix provides an additional unique identification for each build. ## Example usages diff --git a/website/docs/reference/data-test-configs.md b/website/docs/reference/data-test-configs.md index e7adc266b07..0044a707db1 100644 --- a/website/docs/reference/data-test-configs.md +++ b/website/docs/reference/data-test-configs.md @@ -275,3 +275,24 @@ tests: ``` + +#### Specify custom configurations for generic data tests + +Beginning in dbt v1.9, you can use any custom config key to specify custom configurations for data tests. For example, the following specifies the `snowflake_warehouse` custom config that dbt should use when executing the `accepted_values` data test: + +```yml + +models: + - name: my_model + columns: + - name: color + tests: + - accepted_values: + values: ['blue', 'red'] + config: + severity: warn + snowflake_warehouse: my_warehouse + +``` + +Given the config, the data test runs on a different Snowflake virtual warehouse than the one in your default connection to enable better price-performance with a different warehouse size or more granular cost allocation and visibility. diff --git a/website/docs/reference/dbt-classes.md b/website/docs/reference/dbt-classes.md index 13f9263e545..a6a8c2d4fa6 100644 --- a/website/docs/reference/dbt-classes.md +++ b/website/docs/reference/dbt-classes.md @@ -98,9 +98,14 @@ col.numeric_type('numeric', 12, 4) # numeric(12,4) ### Properties -- **name**: Returns the name of the column +- **char_size**: Returns the maximum size for character varying columns +- **column**: Returns the name of the column +- **data_type**: Returns the data type of the column (with size/precision/scale included) +- **dtype**: Returns the data type of the column (without any size/precision/scale included) +- **name**: Returns the name of the column (identical to `column`, provided as an alias). +- **numeric_precision**: Returns the maximum precision for fixed decimal columns +- **numeric_scale**: Returns the maximum scale for fixed decimal columns - **quoted**: Returns the name of the column wrapped in quotes -- **data_type**: Returns the data type of the column ### Instance methods diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md index ca9a7725eb2..9cbc5e5e38b 100644 --- a/website/docs/reference/dbt-commands.md +++ b/website/docs/reference/dbt-commands.md @@ -34,10 +34,10 @@ Commands with a ('❌') indicate write commands, commands with a ('✅') indicat | Command | Description | Parallel execution |
Caveats
| |---------|-------------| :-----------------:| ------------------------------------------ | -| [build](/reference/commands/build) | Build and test all selected resources (models, seeds, snapshots, tests) | ❌ | All tools
All [supported versions](/docs/dbt-versions/core) | +| [build](/reference/commands/build) | Builds and tests all selected resources (models, seeds, snapshots, tests) | ❌ | All tools
All [supported versions](/docs/dbt-versions/core) | | cancel | Cancels the most recent invocation. | N/A | dbt Cloud CLI
Requires [dbt v1.6 or higher](/docs/dbt-versions/core) | | [clean](/reference/commands/clean) | Deletes artifacts present in the dbt project | ✅ | All tools
All [supported versions](/docs/dbt-versions/core) | -| [clone](/reference/commands/clone) | Clone selected models from the specified state | ❌ | All tools
Requires [dbt v1.6 or higher](/docs/dbt-versions/core) | +| [clone](/reference/commands/clone) | Clones selected models from the specified state | ❌ | All tools
Requires [dbt v1.6 or higher](/docs/dbt-versions/core) | | [compile](/reference/commands/compile) | Compiles (but does not run) the models in a project | ✅ | All tools
All [supported versions](/docs/dbt-versions/core) | | [debug](/reference/commands/debug) | Debugs dbt connections and projects | ✅ | dbt Cloud IDE, dbt Core
All [supported versions](/docs/dbt-versions/core) | | [deps](/reference/commands/deps) | Downloads dependencies for a project | ✅ | All tools
All [supported versions](/docs/dbt-versions/core) | @@ -50,9 +50,9 @@ Commands with a ('❌') indicate write commands, commands with a ('✅') indicat | reattach | Reattaches to the most recent invocation to retrieve logs and artifacts. | N/A | dbt Cloud CLI
Requires [dbt v1.6 or higher](/docs/dbt-versions/core) | | [retry](/reference/commands/retry) | Retry the last run `dbt` command from the point of failure | ❌ | All tools
Requires [dbt v1.6 or higher](/docs/dbt-versions/core) | | [run](/reference/commands/run) | Runs the models in a project | ❌ | All tools
All [supported versions](/docs/dbt-versions/core) | -| [run-operation](/reference/commands/run-operation) | Invoke a macro, including running arbitrary maintenance SQL against the database | ❌ | All tools
All [supported versions](/docs/dbt-versions/core) | +| [run-operation](/reference/commands/run-operation) | Invokes a macro, including running arbitrary maintenance SQL against the database | ❌ | All tools
All [supported versions](/docs/dbt-versions/core) | | [seed](/reference/commands/seed) | Loads CSV files into the database | ❌ | All tools
All [supported versions](/docs/dbt-versions/core) | -| [show](/reference/commands/show) | Preview table rows post-transformation | ✅ | All tools
All [supported versions](/docs/dbt-versions/core) | +| [show](/reference/commands/show) | Previews table rows post-transformation | ✅ | All tools
All [supported versions](/docs/dbt-versions/core) | | [snapshot](/reference/commands/snapshot) | Executes "snapshot" jobs defined in a project | ❌ | All tools
All [supported versions](/docs/dbt-versions/core) | | [source](/reference/commands/source) | Provides tools for working with source data (including validating that sources are "fresh") | ✅ | All tools
All [supported versions](/docs/dbt-versions/core) | | [test](/reference/commands/test) | Executes tests defined in a project | ✅ | All tools
All [supported versions](/docs/dbt-versions/core) | diff --git a/website/docs/reference/dbt-jinja-functions/config.md b/website/docs/reference/dbt-jinja-functions/config.md index 3903c82eef7..8083ea2a124 100644 --- a/website/docs/reference/dbt-jinja-functions/config.md +++ b/website/docs/reference/dbt-jinja-functions/config.md @@ -34,13 +34,21 @@ __Args__: The `config.get` function is used to get configurations for a model from the end-user. Configs defined in this way are optional, and a default value can be provided. +There are 3 cases: +1. The configuration variable exists, it is not `None` +1. The configuration variable exists, it is `None` +1. The configuration variable does not exist + Example usage: ```sql {% materialization incremental, default -%} -- Example w/ no default. unique_key will be None if the user does not provide this configuration {%- set unique_key = config.get('unique_key') -%} - -- Example w/ default value. Default to 'id' if 'unique_key' not provided + -- Example w/ alternate value. Use alternative of 'id' if 'unique_key' config is provided, but it is None + {%- set unique_key = config.get('unique_key') or 'id' -%} + + -- Example w/ default value. Default to 'id' if the 'unique_key' config does not exist {%- set unique_key = config.get('unique_key', default='id') -%} ... ``` diff --git a/website/docs/reference/dbt-jinja-functions/model.md b/website/docs/reference/dbt-jinja-functions/model.md index 516981e11e3..b0995ff958c 100644 --- a/website/docs/reference/dbt-jinja-functions/model.md +++ b/website/docs/reference/dbt-jinja-functions/model.md @@ -20,9 +20,9 @@ To view the contents of `model` for a given model: - + -If you're using the CLI, use [log()](/reference/dbt-jinja-functions/log) to print the full contents: +If you're using the command line interface (CLI), use [log()](/reference/dbt-jinja-functions/log) to print the full contents: ```jinja {{ log(model, info=True) }} @@ -42,6 +42,48 @@ If you're using the CLI, use [log()](/reference/dbt-jinja-functions/log) to prin +## Batch properties for microbatch models + +Starting in dbt Core v1.9, the model object includes a `batch` property (`model.batch`), which provides details about the current batch when executing an [incremental microbatch](/docs/build/incremental-microbatch) model. This property is only populated during the batch execution of a microbatch model. + +The following table describes the properties of the `batch` object. Note that dbt appends the property to the `model` and `batch` objects. + +| Property | Description | Example | +| -------- | ----------- | ------- | +| `id` | The unique identifier for the batch within the context of the microbatch model. | `model.batch.id` | +| `event_time_start` | The start time of the batch's [`event_time`](/reference/resource-configs/event-time) filter (inclusive). | `model.batch.event_time_start` | +| `event_time_end` | The end time of the batch's `event_time` filter (exclusive). | `model.batch.event_time_end` | + +### Usage notes + +`model.batch` is only available during the execution of a microbatch model batch. Outside of the microbatch execution, `model.batch` is `None`, and its sub-properties aren't accessible. + +#### Example of safeguarding access to batch properties + +We recommend to always check if `model.batch` is populated before accessing its properties. To do this, use an `if` statement for safe access to `batch` properties: + +```jinja +{% if model.batch %} + {{ log(model.batch.id) }} # Log the batch ID # + {{ log(model.batch.event_time_start) }} # Log the start time of the batch # + {{ log(model.batch.event_time_end) }} # Log the end time of the batch # +{% endif %} +``` + +In this example, the `if model.batch` statement makes sure that the code only runs during a batch execution. `log()` is used to print the `batch` properties for debugging. + +#### Example of log batch details + +This is a practical example of how you might use `model.batch` in a microbatch model to log batch details for the `batch.id`: + +```jinja +{% if model.batch %} + {{ log("Processing batch with ID: " ~ model.batch.id, info=True) }} + {{ log("Batch event time range: " ~ model.batch.event_time_start ~ " to " ~ model.batch.event_time_end, info=True) }} +{% endif %} +``` +In this example, the `if model.batch` statement makes sure that the code only runs during a batch execution. `log()` is used to print the `batch` properties for debugging. + ## Model structure and JSON schema To view the structure of `models` and their definitions: diff --git a/website/docs/reference/dbt-jinja-functions/target.md b/website/docs/reference/dbt-jinja-functions/target.md index 968f64d0f8d..d91749277ac 100644 --- a/website/docs/reference/dbt-jinja-functions/target.md +++ b/website/docs/reference/dbt-jinja-functions/target.md @@ -10,7 +10,7 @@ The `target` variable contains information about your connection to the warehous - **dbt Core:** These values are based on the target defined in your [profiles.yml](/docs/core/connect-data-platform/profiles.yml) file. Please note that for certain adapters, additional configuration steps may be required. Refer to the [set up page](/docs/core/connect-data-platform/about-core-connections) for your data platform. - **dbt Cloud** To learn more about setting up your adapter in dbt Cloud, refer to [About data platform connections](/docs/cloud/connect-data-platform/about-connections). - **[dbt Cloud Scheduler](/docs/deploy/job-scheduler)**: `target.name` is defined per job as described in [Custom target names](/docs/build/custom-target-names). For other attributes, values are defined by the deployment connection. To check these values, click **Deploy** and select **Environments**. Then, select the relevant deployment environment, and click **Settings**. - - **[dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud)**: These values are defined by your connection and credentials. To edit these values, click the gear icon in the top right, select **Profile settings**, and click **Credentials**. Select and edit a project to set up the credentials and target name. + - **[dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud)**: These values are defined by your connection and credentials. To edit these values, click on your account name in the left side menu and select **Account settings**. Then, click **Credentials**. Select and edit a project to set up the credentials and target name. Some configurations are shared between all adapters, while others are adapter-specific. diff --git a/website/docs/reference/dbt-jinja-functions/this.md b/website/docs/reference/dbt-jinja-functions/this.md index f9f2961b08f..7d358cb6299 100644 --- a/website/docs/reference/dbt-jinja-functions/this.md +++ b/website/docs/reference/dbt-jinja-functions/this.md @@ -20,8 +20,6 @@ meta: ## Examples - - ### Configuring incremental models diff --git a/website/docs/reference/dbtignore.md b/website/docs/reference/dbtignore.md index 8733fc592cd..063b455f5cc 100644 --- a/website/docs/reference/dbtignore.md +++ b/website/docs/reference/dbtignore.md @@ -20,6 +20,13 @@ another-non-dbt-model.py # ignore all .py files with "codegen" in the filename *codegen*.py + +# ignore all folders in a directory +path/to/folders/** + +# ignore some folders in a directory +path/to/folders/subfolder/** + ``` diff --git a/website/docs/reference/global-configs/about-global-configs.md b/website/docs/reference/global-configs/about-global-configs.md index 64d56d002fe..435a86d84ba 100644 --- a/website/docs/reference/global-configs/about-global-configs.md +++ b/website/docs/reference/global-configs/about-global-configs.md @@ -95,5 +95,5 @@ Because the values of `flags` can differ across invocations, we strongly advise | [use_experimental_parser](/reference/global-configs/parsing#experimental-parser) | boolean | False | ✅ | `DBT_USE_EXPERIMENTAL_PARSER` | `--use-experimental-parser`, `--no-use-experimental-parser` | ❌ | | [version_check](/reference/global-configs/version-compatibility) | boolean | varies | ✅ | `DBT_VERSION_CHECK` | `--version-check`, `--no-version-check` | ❌ | | [warn_error_options](/reference/global-configs/warnings) | dict | {} | ✅ | `DBT_WARN_ERROR_OPTIONS` | `--warn-error-options` | ✅ | -| [warn_error](/reference/global-configs/warnings) | boolean | False | ✅ | `DBT_WARN_ERROR` | `--warn-error`, `--no-warn-error` | ✅ | +| [warn_error](/reference/global-configs/warnings) | boolean | False | ✅ | `DBT_WARN_ERROR` | `--warn-error` | ✅ | | [write_json](/reference/global-configs/json-artifacts) | boolean | True | ✅ | `DBT_WRITE_JSON` | `--write-json`, `--no-write-json` | ✅ | diff --git a/website/docs/reference/global-configs/behavior-changes.md b/website/docs/reference/global-configs/behavior-changes.md index 299674ae9c1..bda4d2b361a 100644 --- a/website/docs/reference/global-configs/behavior-changes.md +++ b/website/docs/reference/global-configs/behavior-changes.md @@ -59,21 +59,24 @@ flags: source_freshness_run_project_hooks: False restrict_direct_pg_catalog_access: False require_yaml_configuration_for_mf_time_spines: False + require_batched_execution_for_custom_microbatch_strategy: False ``` -When we use dbt Cloud in the following table, we're referring to accounts that have gone "[Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless)." This table outlines which version of dbt Core contains the behavior change or the date the behavior change was added to dbt Cloud. +This table outlines which month of the "Latest" release track in dbt Cloud and which version of dbt Core contains the behavior change's introduction (disabled by default) or maturity (enabled by default). -| Flag | dbt Cloud: Intro | dbt Cloud: Maturity | dbt Core: Intro | dbt Core: Maturity | +| Flag | dbt Cloud "Latest": Intro | dbt Cloud "Latest": Maturity | dbt Core: Intro | dbt Core: Maturity | |-----------------------------------------------------------------|------------------|---------------------|-----------------|--------------------| | [require_explicit_package_overrides_for_builtin_materializations](#package-override-for-built-in-materialization) | 2024.04 | 2024.06 | 1.6.14, 1.7.14 | 1.8.0 | -| [require_resource_names_without_spaces](#no-spaces-in-resource-names) | 2024.05 | TBD* | 1.8.0 | 1.9.0 | -| [source_freshness_run_project_hooks](#project-hooks-with-source-freshness) | 2024.03 | TBD* | 1.8.0 | 1.9.0 | +| [require_resource_names_without_spaces](#no-spaces-in-resource-names) | 2024.05 | TBD* | 1.8.0 | 1.10.0 | +| [source_freshness_run_project_hooks](#project-hooks-with-source-freshness) | 2024.03 | TBD* | 1.8.0 | 1.10.0 | | [Redshift] [restrict_direct_pg_catalog_access](/reference/global-configs/redshift-changes#the-restrict_direct_pg_catalog_access-flag) | 2024.09 | TBD* | dbt-redshift v1.9.0 | 1.9.0 | | [skip_nodes_if_on_run_start_fails](#failures-in-on-run-start-hooks) | 2024.10 | TBD* | 1.9.0 | TBD* | | [state_modified_compare_more_unrendered_values](#source-definitions-for-state) | 2024.10 | TBD* | 1.9.0 | TBD* | | [require_yaml_configuration_for_mf_time_spines](#metricflow-time-spine-yaml) | 2024.10 | TBD* | 1.9.0 | TBD* | +| [require_batched_execution_for_custom_microbatch_strategy](#custom-microbatch-strategy) | 2024.11 | TBD* | 1.9.0 | TBD* | +| [cumulative_type_params](#cumulative-metrics-parameter) | 2024.11 | TBD* | 1.9.0 | TBD* | When the dbt Cloud Maturity is "TBD," it means we have not yet determined the exact date when these flags' default values will change. Affected users will see deprecation warnings in the meantime, and they will receive emails providing advance warning ahead of the maturity date. In the meantime, if you are seeing a deprecation warning, you can either: - Migrate your project to support the new behavior, and then set the flag to `True` to stop seeing the warnings. @@ -164,3 +167,61 @@ In previous versions (dbt Core 1.8 and earlier), the MetricFlow time spine confi When the flag is set to `True`, dbt will continue to support the SQL file configuration. When the flag is set to `False`, dbt will raise a deprecation warning if it detects a MetricFlow time spine configured in a SQL file. The MetricFlow YAML file should have the `time_spine:` field. Refer to [MetricFlow timespine](/docs/build/metricflow-time-spine) for more details. + +### Custom microbatch strategy +The `require_batched_execution_for_custom_microbatch_strategy` flag is set to `False` by default and is only relevant if you already have a custom microbatch macro in your project. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the [microbatch strategy](/docs/build/incremental-microbatch#how-microbatch-compares-to-other-incremental-strategies). + +Set the flag is set to `True` if you have a custom microbatch macro set up in your project. When the flag is set to `True`, dbt will execute the custom microbatch strategy in batches. + +If you have a custom microbatch macro and the flag is left as `False`, dbt will issue a deprecation warning. + +Previously, users needed to set the `DBT_EXPERIMENTAL_MICROBATCH` environment variable to `True` to prevent unintended interactions with existing custom incremental strategies. But this is no longer necessary, as setting `DBT_EXPERMINENTAL_MICROBATCH` will no longer have an effect on runtime functionality. + +### Cumulative metrics + +[Cumulative-type metrics](/docs/build/cumulative#parameters) are nested under the `cumulative_type_params` field in [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks), dbt Core v1.9 and newer. Currently, dbt will warn users if they have cumulative metrics improperly nested. To enforce the new format (resulting in an error instead of a warning), set the `require_nested_cumulative_type_params` to `True`. + +Use the following metric configured with the syntax before v1.9 as an example: + +```yaml + + type: cumulative + type_params: + measure: order_count + window: 7 days + +``` + +If you run `dbt parse` with that syntax on Core v1.9 or [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks), you will receive a warning like: + +```bash + +15:36:22 [WARNING]: Cumulative fields `type_params.window` and +`type_params.grain_to_date` has been moved and will soon be deprecated. Please +nest those values under `type_params.cumulative_type_params.window` and +`type_params.cumulative_type_params.grain_to_date`. See documentation on +behavior changes: +https://docs.getdbt.com/reference/global-configs/behavior-changes. + +``` + +If you set `require_nested_cumulative_type_params` to `True` and re-run `dbt parse` you will now receive an error like: + +```bash + +21:39:18 Cumulative fields `type_params.window` and `type_params.grain_to_date` should be nested under `type_params.cumulative_type_params.window` and `type_params.cumulative_type_params.grain_to_date`. Invalid metrics: orders_last_7_days. See documentation on behavior changes: https://docs.getdbt.com/reference/global-configs/behavior-changes. + +``` + +Once the metric is updated, it will work as expected: + +```yaml + + type: cumulative + type_params: + measure: + name: order_count + cumulative_type_params: + window: 7 days + +``` diff --git a/website/docs/reference/global-configs/cache.md b/website/docs/reference/global-configs/cache.md index 1a74fef8d30..03f33286aa4 100644 --- a/website/docs/reference/global-configs/cache.md +++ b/website/docs/reference/global-configs/cache.md @@ -6,7 +6,7 @@ sidebar: "Cache" ### Cache population -At the start of runs, dbt caches metadata about all the objects in all the schemas where it might materialize resources (such as models). By default, dbt populates the cache with information on all schemas related to the project. +At the start of runs, dbt caches metadata about all the objects in all the schemas where it might materialize resources (such as models). By default, dbt populates the relational cache with information on all schemas related to the project. There are two ways to optionally modify this behavior: - `POPULATE_CACHE` (default: `True`): Whether to populate the cache at all. To skip cache population entirely, use the `--no-populate-cache` flag or `DBT_POPULATE_CACHE: False`. Note that this does not _disable_ the cache; missed cache lookups will run queries, and update the cache afterward. @@ -26,3 +26,11 @@ Or, to improve speed and performance while focused on developing Salesforce mode dbt --cache-selected-only run --select salesforce ``` + +### Logging relational cache events + +import LogLevel from '/snippets/_log-relational-cache.md'; + + diff --git a/website/docs/reference/global-configs/indirect-selection.md b/website/docs/reference/global-configs/indirect-selection.md index 729176a1ff4..03048b57119 100644 --- a/website/docs/reference/global-configs/indirect-selection.md +++ b/website/docs/reference/global-configs/indirect-selection.md @@ -6,7 +6,7 @@ sidebar: "Indirect selection" import IndirSelect from '/snippets/_indirect-selection-definitions.md'; -Use the `--indirect_selection` flag to `dbt test` or `dbt build` to configure which tests to run for the nodes you specify. You can set this as a CLI flag or an environment variable. In dbt Core, you can also configure user configurations in [YAML selectors](/reference/node-selection/yaml-selectors) or in the `flags:` block of `dbt_project.yml`, which sets project-level flags. +Use the `--indirect-selection` flag to `dbt test` or `dbt build` to configure which tests to run for the nodes you specify. You can set this as a CLI flag or an environment variable. In dbt Core, you can also configure user configurations in [YAML selectors](/reference/node-selection/yaml-selectors) or in the `flags:` block of `dbt_project.yml`, which sets project-level flags. When all flags are set, the order of precedence is as follows. Refer to [About global configs](/reference/global-configs/about-global-configs) for more details: diff --git a/website/docs/reference/global-configs/logs.md b/website/docs/reference/global-configs/logs.md index 972a731854d..85969a5bc02 100644 --- a/website/docs/reference/global-configs/logs.md +++ b/website/docs/reference/global-configs/logs.md @@ -66,19 +66,28 @@ See [structured logging](/reference/events-logging#structured-logging) for more The `LOG_LEVEL` config sets the minimum severity of events captured in the console and file logs. This is a more flexible alternative to the `--debug` flag. The available options for the log levels are `debug`, `info`, `warn`, `error`, or `none`. -Setting the `--log-level` will configure console and file logs. +- Setting the `--log-level` will configure console and file logs. + ```text + dbt --log-level debug run + ``` -```text -dbt --log-level debug run -``` +- Setting the `LOG_LEVEL` to `none` will disable information from being sent to either the console or file logs. + + ```text + dbt --log-level none + ``` -To set the file log level as a different value than the console, use the `--log-level-file` flag. +- To set the file log level as a different value than the console, use the `--log-level-file` flag. + ```text + dbt --log-level-file error run + ``` -```text -dbt --log-level-file error run -``` +- To only disable writing to the logs file but keep console logs, set `LOG_LEVEL_FILE` config to none. + ```text + dbt --log-level-file none + ``` ### Debug-level logging @@ -137,11 +146,11 @@ You can use either of these parameters to ensure clean output that's compatible ### Logging relational cache events -The `LOG_CACHE_EVENTS` config allows detailed logging for [relational cache](/reference/global-configs/cache) events, which are disabled by default. +import LogLevel from '/snippets/_log-relational-cache.md'; -```text -dbt --log-cache-events compile -``` +relational cache} +/> ### Color diff --git a/website/docs/reference/global-configs/resource-type.md b/website/docs/reference/global-configs/resource-type.md index 431b6c049cb..9a888c73885 100644 --- a/website/docs/reference/global-configs/resource-type.md +++ b/website/docs/reference/global-configs/resource-type.md @@ -6,7 +6,7 @@ sidebar: "resource type" -The `--resource-type` and `--exclude-resource-type` flags include or exclude resource types from the `dbt build`, `dbt clone`, and `dbt list` commands. In Versionless and from dbt v1.9 onwards, these flags are also supported in the `dbt test` command. +The `--resource-type` and `--exclude-resource-type` flags include or exclude resource types from the `dbt build`, `dbt clone`, and `dbt list` commands. In dbt v1.9 onwards, these flags are also supported in the `dbt test` command. diff --git a/website/docs/reference/global-configs/version-compatibility.md b/website/docs/reference/global-configs/version-compatibility.md index 80841678a85..7667dcfda9c 100644 --- a/website/docs/reference/global-configs/version-compatibility.md +++ b/website/docs/reference/global-configs/version-compatibility.md @@ -14,7 +14,7 @@ Running with dbt=1.0.0 Found 13 models, 2 tests, 1 archives, 0 analyses, 204 macros, 2 operations.... ``` -:::info Versionless +:::info dbt Cloud release tracks ::: diff --git a/website/docs/reference/macro-properties.md b/website/docs/reference/macro-properties.md index 91a616ded0d..69a66f308d9 100644 --- a/website/docs/reference/macro-properties.md +++ b/website/docs/reference/macro-properties.md @@ -19,6 +19,7 @@ macros: [description](/reference/resource-properties/description): [docs](/reference/resource-configs/docs): show: true | false + [meta](/reference/resource-configs/meta): {} arguments: - name: [type](/reference/resource-properties/argument-type): diff --git a/website/docs/reference/model-configs.md b/website/docs/reference/model-configs.md index 65133dcb25a..9508cf68ceb 100644 --- a/website/docs/reference/model-configs.md +++ b/website/docs/reference/model-configs.md @@ -104,6 +104,8 @@ models: + + ```yaml models: [](/reference/resource-configs/resource-path): @@ -121,7 +123,29 @@ models: [+](/reference/resource-configs/plus-prefix)[contract](/reference/resource-configs/contract): {} ``` + + + + +```yaml +models: + [](/reference/resource-configs/resource-path): + [+](/reference/resource-configs/plus-prefix)[enabled](/reference/resource-configs/enabled): true | false + [+](/reference/resource-configs/plus-prefix)[tags](/reference/resource-configs/tags): | [] + [+](/reference/resource-configs/plus-prefix)[pre-hook](/reference/resource-configs/pre-hook-post-hook): | [] + [+](/reference/resource-configs/plus-prefix)[post-hook](/reference/resource-configs/pre-hook-post-hook): | [] + [+](/reference/resource-configs/plus-prefix)[database](/reference/resource-configs/database): + [+](/reference/resource-configs/plus-prefix)[schema](/reference/resource-properties/schema): + [+](/reference/resource-configs/plus-prefix)[alias](/reference/resource-configs/alias): + [+](/reference/resource-configs/plus-prefix)[persist_docs](/reference/resource-configs/persist_docs): + [+](/reference/resource-configs/plus-prefix)[full_refresh](/reference/resource-configs/full_refresh): + [+](/reference/resource-configs/plus-prefix)[meta](/reference/resource-configs/meta): {} + [+](/reference/resource-configs/plus-prefix)[grants](/reference/resource-configs/grants): {} + [+](/reference/resource-configs/plus-prefix)[contract](/reference/resource-configs/contract): {} + [+](/reference/resource-configs/plus-prefix)[event_time](/reference/resource-configs/event-time): my_time_field +``` + @@ -131,6 +155,8 @@ models: + + ```yaml version: 2 @@ -150,17 +176,63 @@ models: [grants](/reference/resource-configs/grants): {} [contract](/reference/resource-configs/contract): {} ``` + - + - +```yaml +version: 2 +models: + - name: [] + config: + [enabled](/reference/resource-configs/enabled): true | false + [tags](/reference/resource-configs/tags): | [] + [pre_hook](/reference/resource-configs/pre-hook-post-hook): | [] + [post_hook](/reference/resource-configs/pre-hook-post-hook): | [] + [database](/reference/resource-configs/database): + [schema](/reference/resource-properties/schema): + [alias](/reference/resource-configs/alias): + [persist_docs](/reference/resource-configs/persist_docs): + [full_refresh](/reference/resource-configs/full_refresh): + [meta](/reference/resource-configs/meta): {} + [grants](/reference/resource-configs/grants): {} + [contract](/reference/resource-configs/contract): {} + [event_time](/reference/resource-configs/event-time): my_time_field +``` + + + + + + +```jinja + +{{ config( + [enabled](/reference/resource-configs/enabled)=true | false, + [tags](/reference/resource-configs/tags)="" | [""], + [pre_hook](/reference/resource-configs/pre-hook-post-hook)="" | [""], + [post_hook](/reference/resource-configs/pre-hook-post-hook)="" | [""], + [database](/reference/resource-configs/database)="", + [schema](/reference/resource-properties/schema)="", + [alias](/reference/resource-configs/alias)="", + [persist_docs](/reference/resource-configs/persist_docs)={}, + [meta](/reference/resource-configs/meta)={}, + [grants](/reference/resource-configs/grants)={}, + [contract](/reference/resource-configs/contract)={} +) }} + +``` + + + + ```jinja {{ config( @@ -175,9 +247,11 @@ models: [meta](/reference/resource-configs/meta)={}, [grants](/reference/resource-configs/grants)={}, [contract](/reference/resource-configs/contract)={} + [event_time](/reference/resource-configs/event-time): my_time_field ) }} ``` + diff --git a/website/docs/reference/project-configs/analysis-paths.md b/website/docs/reference/project-configs/analysis-paths.md index 5c3d223a5cb..20e2e65c2ad 100644 --- a/website/docs/reference/project-configs/analysis-paths.md +++ b/website/docs/reference/project-configs/analysis-paths.md @@ -13,12 +13,31 @@ analysis-paths: [directorypath] ## Definition -Specify a custom list of directories where [analyses](/docs/build/analyses) are located. +Specify a custom list of directories where [analyses](/docs/build/analyses) are located. ## Default Without specifying this config, dbt will not compile any `.sql` files as analyses. -However, the [`dbt init` command](/reference/commands/init) populates this value as `analyses` ([source](https://github.com/dbt-labs/dbt-starter-project/blob/HEAD/dbt_project.yml#L15)) +However, the [`dbt init` command](/reference/commands/init) populates this value as `analyses` ([source](https://github.com/dbt-labs/dbt-starter-project/blob/HEAD/dbt_project.yml#L15)). + +import RelativePath from '/snippets/_relative-path.md'; + + + +- ✅ **Do** + - Use relative path: + ```yml + analysis-paths: ["analyses"] + ``` + +- ❌ **Don't** + - Avoid absolute paths: + ```yml + analysis-paths: ["/Users/username/project/analyses"] + ``` ## Examples ### Use a subdirectory named `analyses` diff --git a/website/docs/reference/project-configs/asset-paths.md b/website/docs/reference/project-configs/asset-paths.md index 1fb3cf9f260..effae8bad7f 100644 --- a/website/docs/reference/project-configs/asset-paths.md +++ b/website/docs/reference/project-configs/asset-paths.md @@ -15,8 +15,29 @@ asset-paths: [directorypath] ## Definition Optionally specify a custom list of directories to copy to the `target` directory as part of the `docs generate` command. This is useful for rendering images in your repository in your project documentation. + ## Default -By default, dbt will not copy any additional files as part of docs generate, i.e. `asset-paths: []` + +By default, dbt will not copy any additional files as part of docs generate. For example, `asset-paths: []`. + +import RelativePath from '/snippets/_relative-path.md'; + + + +- ✅ **Do** + - Use relative path: + ```yml + asset-paths: ["assets"] + ``` + +- ❌ **Don't** + - Avoid absolute paths: + ```yml + asset-paths: ["/Users/username/project/assets"] + ``` ## Examples ### Compile files in the `assets` subdirectory as part of `docs generate` diff --git a/website/docs/reference/project-configs/docs-paths.md b/website/docs/reference/project-configs/docs-paths.md index 5481c19c9fd..6cd179201fc 100644 --- a/website/docs/reference/project-configs/docs-paths.md +++ b/website/docs/reference/project-configs/docs-paths.md @@ -30,6 +30,25 @@ By default, dbt will search in all resource paths for docs blocks (i.e. the comb +import RelativePath from '/snippets/_relative-path.md'; + + + +- ✅ **Do** + - Use relative path: + ```yml + docs-paths: ["docs"] + ``` + +- ❌ **Don't** + - Avoid absolute paths: + ```yml + docs-paths: ["/Users/username/project/docs"] + ``` + ## Example Use a subdirectory named `docs` for docs blocks: diff --git a/website/docs/reference/project-configs/macro-paths.md b/website/docs/reference/project-configs/macro-paths.md index 486ec08ffdf..d790899689e 100644 --- a/website/docs/reference/project-configs/macro-paths.md +++ b/website/docs/reference/project-configs/macro-paths.md @@ -16,7 +16,26 @@ macro-paths: [directorypath] Optionally specify a custom list of directories where [macros](/docs/build/jinja-macros#macros) are located. Note that you cannot co-locate models and macros. ## Default -By default, dbt will search for macros in a directory named `macros`, i.e. `macro-paths: ["macros"]` +By default, dbt will search for macros in a directory named `macros`. For example, `macro-paths: ["macros"]`. + +import RelativePath from '/snippets/_relative-path.md'; + + + +- ✅ **Do** + - Use relative path: + ```yml + macro-paths: ["macros"] + ``` + +- ❌ **Don't:** + - Avoid absolute paths: + ```yml + macro-paths: ["/Users/username/project/macros"] + ``` ## Examples ### Use a subdirectory named `custom_macros` instead of `macros` diff --git a/website/docs/reference/project-configs/model-paths.md b/website/docs/reference/project-configs/model-paths.md index a0652432787..44a40c33066 100644 --- a/website/docs/reference/project-configs/model-paths.md +++ b/website/docs/reference/project-configs/model-paths.md @@ -12,10 +12,29 @@ model-paths: [directorypath] ## Definition -Optionally specify a custom list of directories where [models](/docs/build/models) and [sources](/docs/build/sources) are located. +Optionally specify a custom list of directories where [models](/docs/build/models), [sources](/docs/build/sources), and [unit tests](/docs/build/unit-tests) are located. ## Default -By default, dbt will search for models and sources in the `models` directory, i.e. `model-paths: ["models"]` +By default, dbt will search for models and sources in the `models` directory. For example, `model-paths: ["models"]`. + +import RelativePath from '/snippets/_relative-path.md'; + + + +- ✅ **Do** + - Use relative path: + ```yml + model-paths: ["models"] + ``` + +- ❌ **Don't:** + - Avoid absolute paths: + ```yml + model-paths: ["/Users/username/project/models"] + ``` ## Examples ### Use a subdirectory named `transformations` instead of `models` diff --git a/website/docs/reference/project-configs/on-run-start-on-run-end.md b/website/docs/reference/project-configs/on-run-start-on-run-end.md index 74557839f11..347ce54ab63 100644 --- a/website/docs/reference/project-configs/on-run-start-on-run-end.md +++ b/website/docs/reference/project-configs/on-run-start-on-run-end.md @@ -27,8 +27,6 @@ A SQL statement (or list of SQL statements) to be run at the start or end of the ## Examples - - ### Grant privileges on all schemas that dbt uses at the end of a run This leverages the [schemas](/reference/dbt-jinja-functions/schemas) variable that is only available in an `on-run-end` hook. diff --git a/website/docs/reference/project-configs/query-comment.md b/website/docs/reference/project-configs/query-comment.md index 7e654350306..f7f9472e947 100644 --- a/website/docs/reference/project-configs/query-comment.md +++ b/website/docs/reference/project-configs/query-comment.md @@ -30,7 +30,7 @@ query-comment: ## Definition -A string to inject as a comment in each query that dbt runs against your database. This comment can be used to attribute SQL statements to specific dbt resources like models and tests. +A string to inject as a comment in each query that dbt runs against your database. This comment can attribute SQL statements to specific dbt resources like models and tests. The `query-comment` configuration can also call a macro that returns a string. @@ -51,7 +51,7 @@ create view analytics.analytics.orders as ( ## Using the dictionary syntax The dictionary syntax includes two keys: - * `comment` (optional, see above for default): The string to be injected to a query as a comment. + * `comment` (optional, for more information, refer to the [default](#default) section): The string to be injected into a query as a comment. * `append` (optional, default=`false`): Whether a comment should be appended (added to the bottom of a query) or not (i.e. added to the top of a query). By default, comments are added to the top of queries (i.e. `append: false`). This syntax is useful on databases like Snowflake which [remove leading SQL comments](https://docs.snowflake.com/en/release-notes/2017-04.html#queries-leading-comments-removed-during-execution). @@ -275,4 +275,6 @@ The following context variables are available when generating a query comment: | var | See [var](/reference/dbt-jinja-functions/var) | | target | See [target](/reference/dbt-jinja-functions/target) | | connection_name | A string representing the internal name for the connection. This string is generated by dbt. | -| node | A dictionary representation of the parsed node object. Use `node.unique_id`, `node.database`, `node.schema`, etc | +| node | A dictionary representation of the parsed node object. Use `node.unique_id`, `node.database`, `node.schema`, and so on. | + +Note: The `var()` function in `query-comment` macros only access variables passed through the `--vars` argument in the CLI. Variables defined in the vars block of your `dbt_project.yml` are not accessible when generating query comments. diff --git a/website/docs/reference/project-configs/require-dbt-version.md b/website/docs/reference/project-configs/require-dbt-version.md index 97b42e036ec..f659370af4e 100644 --- a/website/docs/reference/project-configs/require-dbt-version.md +++ b/website/docs/reference/project-configs/require-dbt-version.md @@ -22,7 +22,7 @@ When you set this configuration, dbt sends a helpful error message for any user If this configuration is not specified, no version check will occur. -:::info Versionless +:::info dbt Cloud release tracks diff --git a/website/docs/reference/project-configs/seed-paths.md b/website/docs/reference/project-configs/seed-paths.md index 614bda62cd2..53e2902cae0 100644 --- a/website/docs/reference/project-configs/seed-paths.md +++ b/website/docs/reference/project-configs/seed-paths.md @@ -16,10 +16,29 @@ Optionally specify a custom list of directories where [seed](/docs/build/seeds) ## Default -By default, dbt expects seeds to be located in the `seeds` directory, i.e. `seed-paths: ["seeds"]` +By default, dbt expects seeds to be located in the `seeds` directory. For example, `seed-paths: ["seeds"]`. + +import RelativePath from '/snippets/_relative-path.md'; + + + +- ✅ **Do** + - Use relative path: + ```yml + seed-paths: ["seed"] + ``` + +- ❌ **Don't:** + - Avoid absolute paths: + ```yml + seed-paths: ["/Users/username/project/seed"] + ``` ## Examples -### Use a subdirectory named `custom_seeds` instead of `seeds` +### Use a directory named `custom_seeds` instead of `seeds` diff --git a/website/docs/reference/project-configs/snapshot-paths.md b/website/docs/reference/project-configs/snapshot-paths.md index 8319833f1e6..a13697fc705 100644 --- a/website/docs/reference/project-configs/snapshot-paths.md +++ b/website/docs/reference/project-configs/snapshot-paths.md @@ -16,15 +16,35 @@ snapshot-paths: [directorypath] Optionally specify a custom list of directories where [snapshots](/docs/build/snapshots) are located. -In [Versionless](/docs/dbt-versions/versionless-cloud) and on dbt v1.9 and higher, you can co-locate your snapshots with models if they are [defined using the latest YAML syntax](/docs/build/snapshots). +In dbt Core v1.9+, you can co-locate your snapshots with models if they are [defined using the latest YAML syntax](/docs/build/snapshots). -Note that you cannot co-locate models and snapshots. However, in [Versionless](/docs/dbt-versions/versionless-cloud) and on dbt v1.9 and higher, you can co-locate your snapshots with models if they are [defined using the latest YAML syntax](/docs/build/snapshots). +Note that you cannot co-locate models and snapshots. However, in dbt Core v1.9+, you can co-locate your snapshots with models if they are [defined using the latest YAML syntax](/docs/build/snapshots). ## Default -By default, dbt will search for snapshots in the `snapshots` directory, i.e. `snapshot-paths: ["snapshots"]` +By default, dbt will search for snapshots in the `snapshots` directory. For example, `snapshot-paths: ["snapshots"]`. + + +import RelativePath from '/snippets/_relative-path.md'; + + + +- ✅ **Do** + - Use relative path: + ```yml + snapshot-paths: ["snapshots"] + ``` + +- ❌ **Don't:** + - Avoid absolute paths: + ```yml + snapshot-paths: ["/Users/username/project/snapshots"] + ``` ## Examples ### Use a subdirectory named `archives` instead of `snapshots` diff --git a/website/docs/reference/project-configs/test-paths.md b/website/docs/reference/project-configs/test-paths.md index 6749a07d23d..ab816eec973 100644 --- a/website/docs/reference/project-configs/test-paths.md +++ b/website/docs/reference/project-configs/test-paths.md @@ -21,6 +21,25 @@ Without specifying this config, dbt will search for tests in the `tests` directo - Generic test definitions in the `tests/generic` subdirectory - Singular tests (all other files) +import RelativePath from '/snippets/_relative-path.md'; + + + +- ✅ **Do** + - Use relative path: + ```yml + test-paths: ["test"] + ``` + +- ❌ **Don't:** + - Avoid absolute paths: + ```yml + test-paths: ["/Users/username/project/test"] + ``` + ## Examples ### Use a subdirectory named `custom_tests` instead of `tests` for data tests diff --git a/website/docs/reference/resource-configs/alias.md b/website/docs/reference/resource-configs/alias.md index 3f36bbd0d8f..c14804ef2a7 100644 --- a/website/docs/reference/resource-configs/alias.md +++ b/website/docs/reference/resource-configs/alias.md @@ -100,7 +100,7 @@ models: alias: unique_order_id_test ``` -When using `--store-failures`, this would return the name `analytics.finance.orders_order_id_unique_order_id_test` in the database. +When using [`store_failures_as`](/reference/resource-configs/store_failures_as), this would return the name `analytics.finance.orders_order_id_unique_order_id_test` in the database. diff --git a/website/docs/reference/resource-configs/athena-configs.md b/website/docs/reference/resource-configs/athena-configs.md index f871ede9fab..fd5bc663ee7 100644 --- a/website/docs/reference/resource-configs/athena-configs.md +++ b/website/docs/reference/resource-configs/athena-configs.md @@ -109,7 +109,7 @@ lf_grants={ There are some limitations and recommendations that should be considered: - `lf_tags` and `lf_tags_columns` configs support only attaching lf tags to corresponding resources. -- We recommend managing LF Tags permissions somewhere outside dbt. For example, [terraform](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/lakeformation_permissions) or [aws cdk](https://docs.aws.amazon.com/cdk/api/v1/docs/aws-lakeformation-readme.html). +- We recommend managing LF Tags permissions somewhere outside dbt. For example, [terraform](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/lakeformation_permissions) or [aws cdk](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_lakeformation-readme.html). - `data_cell_filters` management can't be automated outside dbt because the filter can't be attached to the table, which doesn't exist. Once you `enable` this config, dbt will set all filters and their permissions during every dbt run. Such an approach keeps the actual state of row-level security configuration after every dbt run and applies changes if they occur: drop, create, and update filters and their permissions. - Any tags listed in `lf_inherited_tags` should be strictly inherited from the database level and never overridden at the table and column level. - Currently, `dbt-athena` does not differentiate between an inherited tag association and an override it made previously. diff --git a/website/docs/reference/resource-configs/batch_size.md b/website/docs/reference/resource-configs/batch_size.md new file mode 100644 index 00000000000..4001545778a --- /dev/null +++ b/website/docs/reference/resource-configs/batch_size.md @@ -0,0 +1,56 @@ +--- +title: "batch_size" +id: "batch-size" +sidebar_label: "batch_size" +resource_types: [models] +description: "dbt uses `batch_size` to determine how large batches are when running a microbatch incremental model." +datatype: hour | day | month | year +--- + +Available in the [dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9 and higher. + +## Definition + +The`batch_size` config determines how large batches are when running a microbatch. Accepted values are `hour`, `day`, `month`, or `year`. You can configure `batch_size` for a [model](/docs/build/models) in your `dbt_project.yml` file, property YAML file, or config block. + +## Examples + +The following examples set `day` as the `batch_size` for the `user_sessions` model. + +Example of the `batch_size` config in the `dbt_project.yml` file: + + + +```yml +models: + my_project: + user_sessions: + +batch_size: day +``` + + +Example in a properties YAML file: + + + +```yml +models: + - name: user_sessions + config: + batch_size: day +``` + + + +Example in sql model config block: + + + +```sql +{{ config( + lookback='day +) }} +``` + + + diff --git a/website/docs/reference/resource-configs/begin.md b/website/docs/reference/resource-configs/begin.md new file mode 100644 index 00000000000..dd47419be21 --- /dev/null +++ b/website/docs/reference/resource-configs/begin.md @@ -0,0 +1,55 @@ +--- +title: "begin" +id: "begin" +sidebar_label: "begin" +resource_types: [models] +description: "dbt uses `begin` to determine when a microbatch incremental model should begin from. When defined on a micorbatch incremental model, `begin` is used as the lower time bound when the model is built for the first time or fully refreshed." +datatype: string +--- + +Available in the [dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9 and higher. + +## Definition + +Set the `begin` config to the timestamp value at which your microbatch model data should begin — at the point the data becomes relevant for the microbatch model. You can configure `begin` for a [model](/docs/build/models) in your `dbt_project.yml` file, property YAML file, or config block. The value for `begin` must be a string representing an ISO formatted date OR date and time. + +## Examples + +The following examples set `2024-01-01 00:00:00` as the `begin` config for the `user_sessions` model. + +Example in the `dbt_project.yml` file: + + + +```yml +models: + my_project: + user_sessions: + +begin: "2024-01-01 00:00:00" +``` + + +Example in a properties YAML file: + + + +```yml +models: + - name: user_sessions + config: + begin: "2024-01-01 00:00:00" +``` + + + +Example in sql model config block: + + + +```sql +{{ config( + begin='2024-01-01 00:00:00' +) }} +``` + + diff --git a/website/docs/reference/resource-configs/bigquery-configs.md b/website/docs/reference/resource-configs/bigquery-configs.md index 9dd39c936b6..c912bca0688 100644 --- a/website/docs/reference/resource-configs/bigquery-configs.md +++ b/website/docs/reference/resource-configs/bigquery-configs.md @@ -425,9 +425,10 @@ Please note that in order for policy tags to take effect, [column-level `persist The [`incremental_strategy` config](/docs/build/incremental-strategy) controls how dbt builds incremental models. dbt uses a [merge statement](https://cloud.google.com/bigquery/docs/reference/standard-sql/dml-syntax) on BigQuery to refresh incremental tables. -The `incremental_strategy` config can be set to one of two values: - - `merge` (default) - - `insert_overwrite` +The `incremental_strategy` config can be set to one of the following values: +- `merge` (default) +- `insert_overwrite` +- [`microbatch`](/docs/build/incremental-microbatch) ### Performance and cost @@ -561,7 +562,7 @@ If no `partitions` configuration is provided, dbt will instead: 3. Query the destination table to find the _max_ partition in the database When building your model SQL, you can take advantage of the introspection performed -by dbt to filter for only _new_ data. The max partition in the destination table +by dbt to filter for only _new_ data. The maximum value in the partitioned field in the destination table will be available using the `_dbt_max_partition` BigQuery scripting variable. **Note:** this is a BigQuery SQL variable, not a dbt Jinja variable, so no jinja brackets are required to access this variable. @@ -908,3 +909,10 @@ By default, this is set to `True` to support the default `intermediate_format` o ### The `intermediate_format` parameter The `intermediate_format` parameter specifies which file format to use when writing records to a table. The default is `parquet`. + + +## Unit test limitations + +You must specify all fields in a BigQuery `STRUCT` for [unit tests](/docs/build/unit-tests). You cannot use only a subset of fields in a `STRUCT`. + + diff --git a/website/docs/reference/resource-configs/database.md b/website/docs/reference/resource-configs/database.md index 338159b30dc..6c57e7e2c69 100644 --- a/website/docs/reference/resource-configs/database.md +++ b/website/docs/reference/resource-configs/database.md @@ -49,7 +49,7 @@ This would result in the generated relation being located in the `staging` datab -Available for versionless dbt Cloud or dbt Core v1.9+. Select v1.9 or newer from the version dropdown to view the configs. +Available for dbt Cloud release tracks or dbt Core v1.9+. Select v1.9 or newer from the version dropdown to view the configs. @@ -79,22 +79,19 @@ This results in the generated relation being located in the `snapshots` database -Configure a database in your `dbt_project.yml` file. +Customize the database for storing test results in your `dbt_project.yml` file. -For example, to load a test into a database called `reporting` instead of the target database, you can configure it like this: +For example, to save test results in a specific database, you can configure it like this: ```yml tests: - - my_not_null_test: - column_name: order_id - type: not_null - +database: reporting + +store_failures: true + +database: test_results ``` -This would result in the generated relation being located in the `reporting` database, so the full relation name would be `reporting.finance.my_not_null_test`. - +This would result in the test results being stored in the `test_results` database. diff --git a/website/docs/reference/resource-configs/databricks-configs.md b/website/docs/reference/resource-configs/databricks-configs.md index f807b1c0d88..6ac3e23c113 100644 --- a/website/docs/reference/resource-configs/databricks-configs.md +++ b/website/docs/reference/resource-configs/databricks-configs.md @@ -51,7 +51,7 @@ We do not yet have a PySpark API to set tblproperties at table creation, so this -dbt Core v.9 and Versionless dbt Clouyd support for `table_format: iceberg`, in addition to all previous table configurations supported in 1.8. +dbt-databricks v1.9 adds support for the `table_format: iceberg` config. Try it now on the [dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks). All other table configurations were also supported in 1.8. | Option | Description | Required? | Model Support | Example | |---------------------|-----------------------------|-------------------------------------------|-----------------|--------------------------| @@ -76,7 +76,7 @@ dbt Core v.9 and Versionless dbt Clouyd support for `table_format: iceberg`, in ### Python submission methods -In dbt v1.9 and higher, or in [Versionless](/docs/dbt-versions/versionless-cloud) dbt Cloud, you can use these four options for `submission_method`: +In dbt-databricks v1.9 (try it now in [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks)), you can use these four options for `submission_method`: * `all_purpose_cluster`: Executes the python model either directly using the [command api](https://docs.databricks.com/api/workspace/commandexecution) or by uploading a notebook and creating a one-off job run * `job_cluster`: Creates a new job cluster to execute an uploaded notebook as a one-off job run @@ -1031,7 +1031,7 @@ The following table summarizes our configuration support: partition_by='id', schedule = { 'cron': '0 0 * * * ? *', - 'time_zone': 'Etc/UTC' + 'time_zone_value': 'Etc/UTC' }, tblproperties={ 'key': 'value' diff --git a/website/docs/reference/resource-configs/dbt_valid_to_current.md b/website/docs/reference/resource-configs/dbt_valid_to_current.md new file mode 100644 index 00000000000..2a6cf3abe6d --- /dev/null +++ b/website/docs/reference/resource-configs/dbt_valid_to_current.md @@ -0,0 +1,116 @@ +--- +resource_types: [snapshots] +description: "Use the `dbt_valid_to_current` config to set a custom indicator for the value of `dbt_valid_to` in current snapshot records" +datatype: "{}" +default_value: {NULL} +id: "dbt_valid_to_current" +--- + +Available from dbt v1.9 or with [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) dbt Cloud. + + + +```yaml +snapshots: + my_project: + +dbt_valid_to_current: "to_date('9999-12-31')" + +``` + + + + + +```sql +{{ + config( + unique_key='id', + strategy='timestamp', + updated_at='updated_at', + dbt_valid_to_current='to_date('9999-12-31')' + ) +}} +``` + + + + + +```yml +snapshots: + [](/reference/resource-configs/resource-path): + +dbt_valid_to_current: "to_date('9999-12-31')" +``` + + + +## Description + +Use the `dbt_valid_to_current` config to set a custom indicator for the value of `dbt_valid_to` in current snapshot records (like a future date). By default, this value is `NULL`. When set, dbt will use this specified value instead of `NULL` for `dbt_valid_to` for current records in the snapshot table. + +This approach makes it easier to assign a custom date, work in a join, or perform range-based filtering that requires an end date. + +:::warning + +To avoid any unintentional data modification, dbt will _not_ automatically adjust the current value in the existing `dbt_valid_to` column. Existing current records will still have `dbt_valid_to` set to `NULL`. + +Any new records inserted _after_ applying the `dbt_valid_to_current` configuration will have `dbt_valid_to` set to the specified value (like '9999-12-31'), instead of the default `NULL` value. + +::: + +### Considerations + +- **Date expressions** — Provide a hardcoded date expression compatible with your data platform, such as to_date`('9999-12-31')`. Note that syntax may vary by warehouse (for example, `to_date('YYYY-MM-DD'`) or `date(YYYY, MM, DD)`). + +- **Jinja limitation** — `dbt_valid_to_current` only accepts static SQL expressions. Jinja expressions (like `{{ var('my_future_date') }}`) are not supported. + +- **Deferral and `state:modified`** — Changes to `dbt_valid_to_current` are compatible with deferral and `--select state:modified`. When this configuration changes, it'll appear in `state:modified` selections, raising a warning to manually make the necessary snapshot updates. + +## Default + +By default, `dbt_valid_to` is set to `NULL` for current (most recent) records in your snapshot table. This means that these records are still valid and have no defined end date. + +If you prefer to use a specific value instead of `NULL` for `dbt_valid_to` in current and future records, you can use the `dbt_valid_to_current` configuration option. For example, setting a date in the far future, `9999-12-31`. + +The value assigned to `dbt_valid_to_current` should be a string representing a valid date or timestamp, depending on your database's requirements. Use expressions that work within the data platform. + + +## Impact on snapshot records + +When you set `dbt_valid_to_current`, it affects how dbt manages the `dbt_valid_to` column in your snapshot table: + +- **For existing records** — To avoid any unintentional data modification, dbt will _not_ automatically adjust the current value in the existing `dbt_valid_to` column. Existing current records will still have `dbt_valid_to` set to `NULL`. + +- **For new records** — Any new records inserted after applying the `dbt_valid_to_current` configuration will have `dbt_valid_to` set to the specified value (for example, '9999-12-31'), instead of `NULL`. + +This means your snapshot table will have current records with `dbt_valid_to` values of both `NULL` (from existing data) and the new specified value (from new data). If you'd rather have consistent `dbt_valid_to` values for current records, you can manually update existing records in your snapshot table (where `dbt_valid_to` is `NULL`) to match your `dbt_valid_to_current` value. + +## Example + + + +```yaml +snapshots: + - name: my_snapshot + config: + strategy: timestamp + updated_at: updated_at + dbt_valid_to_current: "to_date('9999-12-31')" + columns: + - name: dbt_valid_from + description: The timestamp when the record became valid. + - name: dbt_valid_to + description: > + The timestamp when the record ceased to be valid. For current records, + this is either `NULL` or the value specified in `dbt_valid_to_current` + (like `'9999-12-31'`). +``` + + + +The resulting snapshot table contains the configured dbt_valid_to column value: + +| id | dbt_scd_id | dbt_updated_at | dbt_valid_from | dbt_valid_to | +| -- | -------------------- | -------------------- | -------------------- | -------------------- | +| 1 | 60a1f1dbdf899a4dd... | 2024-10-02 ... | 2024-10-02 ... | 9999-12-31 ... | +| 2 | b1885d098f8bcff51... | 2024-10-02 ... | 2024-10-02 ... | 9999-12-31 ... | diff --git a/website/docs/reference/resource-configs/event-time.md b/website/docs/reference/resource-configs/event-time.md new file mode 100644 index 00000000000..c18c8de6397 --- /dev/null +++ b/website/docs/reference/resource-configs/event-time.md @@ -0,0 +1,284 @@ +--- +title: "event_time" +id: "event-time" +sidebar_label: "event_time" +resource_types: [models, seeds, source] +description: "dbt uses event_time to understand when an event occurred. When defined, event_time enables microbatch incremental models and more refined comparison of datasets during Advanced CI." +datatype: string +--- + +Available in [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9 and higher. + + + + + + +```yml +models: + [resource-path:](/reference/resource-configs/resource-path) + +event_time: my_time_field +``` + + + + + +```yml +models: + - name: model_name + [config](/reference/resource-properties/config): + event_time: my_time_field +``` + + + + +```sql +{{ config( + event_time='my_time_field' +) }} +``` + + + + + + + + + +```yml +seeds: + [resource-path:](/reference/resource-configs/resource-path) + +event_time: my_time_field +``` + + + + +```yml +seeds: + - name: seed_name + [config](/reference/resource-properties/config): + event_time: my_time_field +``` + + + + + + + + +```yml +snapshots: + [resource-path:](/reference/resource-configs/resource-path) + +event_time: my_time_field +``` + + + + + +```yml +snapshots: + - name: snapshot_name + [config](/reference/resource-properties/config): + event_time: my_time_field +``` + + + + + + + +```sql + +{{ config( + event_time: 'my_time_field' +) }} +``` + + + + +import SnapshotYaml from '/snippets/_snapshot-yaml-spec.md'; + + + + + + + + + + + + +```yml +sources: + [resource-path:](/reference/resource-configs/resource-path) + +event_time: my_time_field +``` + + + + +```yml +sources: + - name: source_name + [config](/reference/resource-properties/config): + event_time: my_time_field +``` + + + + + +## Definition + +Set the `event_time` to the name of the field that represents the timestamp of the event -- "at what time did the row occur" -- as opposed to an event ingestion date. You can configure `event_time` for a [model](/docs/build/models), [seed](/docs/build/seeds), or [source](/docs/build/sources) in your `dbt_project.yml` file, property YAML file, or config block. + +Here are some examples of good and bad `event_time` columns: + +- ✅ Good: + - `account_created_at` — This represents the specific time when an account was created, making it a fixed event in time. + - `session_began_at` — This captures the exact timestamp when a user session started, which won’t change and directly ties to the event. + +- ❌ Bad: + + - `_fivetran_synced` — This isn't the time that the event happened, it's the time that the event was ingested. + - `last_updated_at` — This isn't a good use case as this will keep changing over time. + +`event_time` is required for [Incremental microbatch](/docs/build/incremental-microbatch) and highly recommended for [Advanced CI's compare changes](/docs/deploy/advanced-ci#optimizing-comparisons) in CI/CD workflows, where it ensures the same time-slice of data is correctly compared between your CI and production environments. + +## Examples + + + + + +Here's an example in the `dbt_project.yml` file: + + + +```yml +models: + my_project: + user_sessions: + +event_time: session_start_time +``` + + +Example in a properties YAML file: + + + +```yml +models: + - name: user_sessions + config: + event_time: session_start_time +``` + + + +Example in sql model config block: + + + +```sql +{{ config( + event_time='session_start_time' +) }} +``` + + + +This setup sets `session_start_time` as the `event_time` for the `user_sessions` model. + + + + +Here's an example in the `dbt_project.yml` file: + + + +```yml +seeds: + my_project: + my_seed: + +event_time: record_timestamp +``` + + + +Example in a seed properties YAML: + + + +```yml +seeds: + - name: my_seed + config: + event_time: record_timestamp +``` + + +This setup sets `record_timestamp` as the `event_time` for `my_seed`. + + + + + +Here's an example in the `dbt_project.yml` file: + + + +```yml +snapshots: + my_project: + my_snapshot: + +event_time: record_timestamp +``` + + + +Example in a snapshot properties YAML: + + + +```yml +snapshots: + - name: my_snapshot + config: + event_time: record_timestamp +``` + + +This setup sets `record_timestamp` as the `event_time` for `my_snapshot`. + + + + + +Here's an example of source properties YAML file: + + + +```yml +sources: + - name: source_name + tables: + - name: table_name + config: + event_time: event_timestamp +``` + + +This setup sets `event_timestamp` as the `event_time` for the specified source table. + + + diff --git a/website/docs/reference/resource-configs/hard-deletes.md b/website/docs/reference/resource-configs/hard-deletes.md new file mode 100644 index 00000000000..50c8046f4e1 --- /dev/null +++ b/website/docs/reference/resource-configs/hard-deletes.md @@ -0,0 +1,111 @@ +--- +title: hard_deletes +resource_types: [snapshots] +description: "Use the `hard_deletes` config to control how deleted rows are tracked in your snapshot table." +datatype: "boolean" +default_value: {ignore} +id: "hard-deletes" +sidebar_label: "hard_deletes" +--- + +Available from dbt v1.9 or with [dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks). + + + + +```yaml +snapshots: + - name: + config: + hard_deletes: 'ignore' | 'invalidate' | 'new_record' +``` + + + + +```yml +snapshots: + [](/reference/resource-configs/resource-path): + +hard_deletes: "ignore" | "invalidate" | "new_record" +``` + + + + + +```sql +{{ + config( + unique_key='id', + strategy='timestamp', + updated_at='updated_at', + hard_deletes='ignore' | 'invalidate' | 'new_record' + ) +}} +``` + + + + +## Description + +The `hard_deletes` config gives you more control on how to handle deleted rows from the source. Supported options are `ignore` (default), `invalidate` (replaces the legacy `invalidate_hard_deletes=true`), and `new_record`. Note that `new_record` will create a new metadata column in the snapshot table. + +import HardDeletes from '/snippets/_hard-deletes.md'; + + + +:::warning + +If you're updating an existing snapshot to use the `hard_deletes` config, dbt _will not_ handle migrations automatically. We recommend either only using these settings for net-new snapshots, or [arranging an update](/reference/snapshot-configs#snapshot-configuration-migration) of pre-existing tables before enabling this setting. +::: + +## Default + +By default, if you don’t specify `hard_deletes`, it'll automatically default to `ignore`. Deleted rows will not be tracked and their `dbt_valid_to` column remains `NULL`. + +The `hard_deletes` config has three methods: + +| Methods | Description | +| --------- | ----------- | +| `ignore` (default) | No action for deleted records. | +| `invalidate` | Behaves the same as the existing `invalidate_hard_deletes=true`, where deleted records are invalidated by setting `dbt_valid_to` to current time. This method replaces the `invalidate_hard_deletes` config to give you more control on how to handle deleted rows from the source. | +| `new_record` | Tracks deleted records as new rows using the `dbt_is_deleted` meta field when records are deleted.| + +## Considerations +- **Backward compatibility**: The `invalidate_hard_deletes` config is still supported for existing snapshots but can't be used alongside `hard_deletes`. +- **New snapshots**: For new snapshots, we recommend using `hard_deletes` instead of `invalidate_hard_deletes`. +- **Migration**: If you switch an existing snapshot to use `hard_deletes` without migrating your data, you may encounter inconsistent or incorrect results, such as a mix of old and new data formats. + +## Example + + + +```yaml +snapshots: + - name: my_snapshot + config: + hard_deletes: new_record # options are: 'ignore', 'invalidate', or 'new_record' + strategy: timestamp + updated_at: updated_at + columns: + - name: dbt_valid_from + description: Timestamp when the record became valid. + - name: dbt_valid_to + description: Timestamp when the record stopped being valid. + - name: dbt_is_deleted + description: Indicates whether the record was deleted. +``` + + + +The resulting snapshot table contains the `hard_deletes: new_record` configuration. If a record is deleted and later restored, the resulting snapshot table might look like this: + +| id | dbt_scd_id | Status | dbt_updated_at | dbt_valid_from | dbt_valid_to | dbt_is_deleted | +| -- | -------------------- | ----- | -------------------- | --------------------| -------------------- | ----------- | +| 1 | 60a1f1dbdf899a4dd... | pending | 2024-10-02 ... | 2024-05-19... | 2024-05-20 ... | False | +| 1 | b1885d098f8bcff51... | pending | 2024-10-02 ... | 2024-05-20 ... | 2024-06-03 ... | True | +| 1 | b1885d098f8bcff53... | shipped | 2024-10-02 ... | 2024-06-03 ... | | False | +| 2 | b1885d098f8bcff55... | active | 2024-10-02 ... | 2024-05-19 ... | | False | + +In this example, the `dbt_is_deleted` column is set to `True` when the record is deleted. When the record is restored, the `dbt_is_deleted` column is set to `False`. diff --git a/website/docs/reference/resource-configs/invalidate_hard_deletes.md b/website/docs/reference/resource-configs/invalidate_hard_deletes.md index bdaec7e33a9..67123487fa1 100644 --- a/website/docs/reference/resource-configs/invalidate_hard_deletes.md +++ b/website/docs/reference/resource-configs/invalidate_hard_deletes.md @@ -1,9 +1,17 @@ --- +title: invalidate_hard_deletes (legacy) resource_types: [snapshots] description: "Invalidate_hard_deletes - Read this in-depth guide to learn about configurations in dbt." datatype: column_name +sidebar_label: invalidate_hard_deletes (legacy) --- +:::warning This is a legacy config — Use the [`hard_deletes`](/reference/resource-configs/hard-deletes) config instead. + +In Versionless and dbt Core 1.9 and higher, the [`hard_deletes`](/reference/resource-configs/hard-deletes) config replaces the `invalidate_hard_deletes` config for better control over how to handle deleted rows from the source. + +For new snapshots, set the config to `hard_deletes='invalidate'` instead of `invalidate_hard_deletes=true`. For existing snapshots, [arrange an update](/reference/snapshot-configs#snapshot-configuration-migration) of pre-existing tables before enabling this setting. Refer to +::: diff --git a/website/docs/reference/resource-configs/lookback.md b/website/docs/reference/resource-configs/lookback.md new file mode 100644 index 00000000000..037ffdeb68f --- /dev/null +++ b/website/docs/reference/resource-configs/lookback.md @@ -0,0 +1,55 @@ +--- +title: "lookback" +id: "lookback" +sidebar_label: "lookback" +resource_types: [models] +description: "dbt uses `lookback` to detrmine how many 'batches' of `batch_size` to reprocesses when a microbatch incremental model is running incrementally." +datatype: int +--- + +Available in the [dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9 and higher. + +## Definition + +Set the `lookback` to an integer greater than or equal to zero. The default value is `1`. You can configure `lookback` for a [model](/docs/build/models) in your `dbt_project.yml` file, property YAML file, or config block. + +## Examples + +The following examples set `2` as the `lookback` config for the `user_sessions` model. + +Example in the `dbt_project.yml` file: + + + +```yml +models: + my_project: + user_sessions: + +lookback: 2 +``` + + +Example in a properties YAML file: + + + +```yml +models: + - name: user_sessions + config: + lookback: 2 +``` + + + +Example in sql model config block: + + + +```sql +{{ config( + lookback=2 +) }} +``` + + diff --git a/website/docs/reference/resource-configs/meta.md b/website/docs/reference/resource-configs/meta.md index 53a4f77184e..e1542bdbc82 100644 --- a/website/docs/reference/resource-configs/meta.md +++ b/website/docs/reference/resource-configs/meta.md @@ -56,7 +56,7 @@ See [configs and properties](/reference/configs-and-properties) for details. ```yml version: 2 -sources: +[sources](/reference/source-properties): - name: model_name config: meta: {} @@ -110,7 +110,7 @@ version: 2 snapshots: - name: snapshot_name config: - meta: {} + [meta](/reference/snapshot-properties): {} columns: - name: column_name @@ -147,7 +147,7 @@ The `meta` config is not currently supported for analyses. ```yml version: 2 -macros: +[macros](/reference/macro-properties): - name: macro_name meta: {} @@ -287,7 +287,7 @@ models: ```yml version: 2 -sources: +[sources](/reference/source-properties): - name: salesforce tables: diff --git a/website/docs/reference/resource-configs/postgres-configs.md b/website/docs/reference/resource-configs/postgres-configs.md index f2bf90a93c0..e71c6f1484d 100644 --- a/website/docs/reference/resource-configs/postgres-configs.md +++ b/website/docs/reference/resource-configs/postgres-configs.md @@ -11,6 +11,7 @@ In dbt-postgres, the following incremental materialization strategies are suppor - `append` (default when `unique_key` is not defined) - `merge` - `delete+insert` (default when `unique_key` is defined) +- [`microbatch`](/docs/build/incremental-microbatch) ## Performance optimizations diff --git a/website/docs/reference/resource-configs/pre-hook-post-hook.md b/website/docs/reference/resource-configs/pre-hook-post-hook.md index bd01a7be840..ee3c81b0fd6 100644 --- a/website/docs/reference/resource-configs/pre-hook-post-hook.md +++ b/website/docs/reference/resource-configs/pre-hook-post-hook.md @@ -160,8 +160,6 @@ import SQLCompilationError from '/snippets/_render-method.md'; ## Examples - - ### [Redshift] Unload one model to S3 diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md index b033cd6267e..01c9bffd055 100644 --- a/website/docs/reference/resource-configs/redshift-configs.md +++ b/website/docs/reference/resource-configs/redshift-configs.md @@ -17,6 +17,7 @@ In dbt-redshift, the following incremental materialization strategies are suppor - `append` (default when `unique_key` is not defined) - `merge` - `delete+insert` (default when `unique_key` is defined) +- [`microbatch`](/docs/build/incremental-microbatch) All of these strategies are inherited from dbt-postgres. diff --git a/website/docs/reference/resource-configs/schema.md b/website/docs/reference/resource-configs/schema.md index 1e2ff47729c..6f56215de61 100644 --- a/website/docs/reference/resource-configs/schema.md +++ b/website/docs/reference/resource-configs/schema.md @@ -50,7 +50,7 @@ This would result in the generated relation being located in the `mappings` sche -Available for versionless dbt Cloud or dbt Core v1.9+. Select v1.9 or newer from the version dropdown to view the configs. +Available in dbt Core v1.9+. Select v1.9 or newer from the version dropdown to view the configs. Try it now in the [dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks). @@ -108,7 +108,9 @@ This would result in the test results being stored in the `test_results` schema. Refer to [Usage](#usage) for more examples. ## Definition -Optionally specify a custom schema for a [model](/docs/build/sql-models) or [seed](/docs/build/seeds). (To specify a schema for a [snapshot](/docs/build/snapshots), use the [`target_schema` config](/reference/resource-configs/target_schema)). +Optionally specify a custom schema for a [model](/docs/build/sql-models), [seed](/docs/build/seeds), [snapshot](/docs/build/snapshots), [saved query](/docs/build/saved-queries), or [test](/docs/build/data-tests). + +For users on dbt Cloud v1.8 or earlier, use the [`target_schema` config](/reference/resource-configs/target_schema) to specify a custom schema for a snapshot. When dbt creates a relation (/) in a database, it creates it as: `{{ database }}.{{ schema }}.{{ identifier }}`, e.g. `analytics.finance.payments` diff --git a/website/docs/reference/resource-configs/snapshot_meta_column_names.md b/website/docs/reference/resource-configs/snapshot_meta_column_names.md index 46aba7886d0..f1d29ba8bee 100644 --- a/website/docs/reference/resource-configs/snapshot_meta_column_names.md +++ b/website/docs/reference/resource-configs/snapshot_meta_column_names.md @@ -6,7 +6,7 @@ default_value: {"dbt_valid_from": "dbt_valid_from", "dbt_valid_to": "dbt_valid_t id: "snapshot_meta_column_names" --- -Starting in 1.9 or with [versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) dbt Cloud. +Available in dbt Core v1.9+. Select v1.9 or newer from the version dropdown to view the configs. Try it now in the [dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks). @@ -19,6 +19,7 @@ snapshots: dbt_valid_to: dbt_scd_id: dbt_updated_at: + dbt_is_deleted: ``` @@ -34,6 +35,7 @@ snapshots: "dbt_valid_to": "", "dbt_scd_id": "", "dbt_updated_at": "", + "dbt_is_deleted": "", } ) }} @@ -52,7 +54,7 @@ snapshots: dbt_valid_to: dbt_scd_id: dbt_updated_at: - + dbt_is_deleted: ``` @@ -71,6 +73,7 @@ By default, dbt snapshots use the following column names to track change history | `dbt_valid_to` | The timestamp when this row is no longer valid. | | | `dbt_scd_id` | A unique key generated for each snapshot row. | This is used internally by dbt. | | `dbt_updated_at` | The `updated_at` timestamp of the source record when this snapshot row was inserted. | This is used internally by dbt. | +| `dbt_is_deleted` | A boolean value indicating if the record has been deleted. `True` if deleted, `False` otherwise. | Added when `hard_deletes='new_record'` is configured. | However, these column names can be customized using the `snapshot_meta_column_names` config. @@ -92,18 +95,21 @@ snapshots: unique_key: id strategy: check check_cols: all + hard_deletes: new_record snapshot_meta_column_names: dbt_valid_from: start_date dbt_valid_to: end_date dbt_scd_id: scd_id dbt_updated_at: modified_date + dbt_is_deleted: is_deleted ``` The resulting snapshot table contains the configured meta column names: -| id | scd_id | modified_date | start_date | end_date | -| -- | -------------------- | -------------------- | -------------------- | -------------------- | -| 1 | 60a1f1dbdf899a4dd... | 2024-10-02 ... | 2024-10-02 ... | 2024-10-02 ... | -| 2 | b1885d098f8bcff51... | 2024-10-02 ... | 2024-10-02 ... | | +| id | scd_id | modified_date | start_date | end_date | is_deleted | +| -- | -------------------- | -------------------- | -------------------- | -------------------- | ---------- | +| 1 | 60a1f1dbdf899a4dd... | 2024-10-02 ... | 2024-10-02 ... | 2024-10-03 ... | False | +| 1 | 60a1f1dbdf899a4dd... | 2024-10-03 ... | 2024-10-03 ... | | True | +| 2 | b1885d098f8bcff51... | 2024-10-02 ... | 2024-10-02 ... | | False | diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md index b95b79241ba..d576b195b65 100644 --- a/website/docs/reference/resource-configs/snowflake-configs.md +++ b/website/docs/reference/resource-configs/snowflake-configs.md @@ -38,11 +38,11 @@ flags: The following configurations are supported. For more information, check out the Snowflake reference for [`CREATE ICEBERG TABLE` (Snowflake as the catalog)](https://docs.snowflake.com/en/sql-reference/sql/create-iceberg-table-snowflake). -| Field | Type | Required | Description | Sample input | Note | -| --------------------- | ------ | -------- | -------------------------------------------------------------------------------------------------------------------------- | ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| Table Format | String | Yes | Configures the objects table format. | `iceberg` | `iceberg` is the only accepted value. | +| Field | Type | Required | Description | Sample input | Note | +| ------ | ----- | -------- | ------------- | ------------ | ------ | +| Table Format | String | Yes | Configures the objects table format. | `iceberg` | `iceberg` is the only accepted value. | | External volume | String | Yes(*) | Specifies the identifier (name) of the external volume where Snowflake writes the Iceberg table's metadata and data files. | `my_s3_bucket` | *You don't need to specify this if the account, database, or schema already has an associated external volume. [More info](https://docs.snowflake.com/en/sql-reference/sql/create-iceberg-table-snowflake#:~:text=Snowflake%20Table%20Structures.-,external_volume) | -| Base location Subpath | String | No | An optional suffix to add to the `base_location` path that dbt automatically specifies. | `jaffle_marketing_folder` | We recommend that you do not specify this. Modifying this parameter results in a new Iceberg table. See [Base Location](#base-location) for more info. | +| Base location Subpath | String | No | An optional suffix to add to the `base_location` path that dbt automatically specifies. | `jaffle_marketing_folder` | We recommend that you do not specify this. Modifying this parameter results in a new Iceberg table. See [Base Location](#base-location) for more info. | ### Example configuration @@ -470,8 +470,15 @@ In this example, you can set up a query tag to be applied to every query with th The [`incremental_strategy` config](/docs/build/incremental-strategy) controls how dbt builds incremental models. By default, dbt will use a [merge statement](https://docs.snowflake.net/manuals/sql-reference/sql/merge.html) on Snowflake to refresh incremental tables. +Snowflake supports the following incremental strategies: +- Merge (default) +- Append +- Delete+insert +- [`microbatch`](/docs/build/incremental-microbatch) + Snowflake's `merge` statement fails with a "nondeterministic merge" error if the `unique_key` specified in your model config is not actually unique. If you encounter this error, you can instruct dbt to use a two-step incremental approach by setting the `incremental_strategy` config for your model to `delete+insert`. + ## Configuring table clustering dbt supports [table clustering](https://docs.snowflake.net/manuals/user-guide/tables-clustering-keys.html) on Snowflake. To control clustering for a or incremental model, use the `cluster_by` config. When this configuration is applied, dbt will do two things: @@ -678,3 +685,27 @@ Per the [Snowflake documentation](https://docs.snowflake.com/en/sql-reference/in >- DDL operations. >- DML operations (for tables only). >- Background maintenance operations on metadata performed by Snowflake. + + + +## Pagination for object results + +By default, when dbt encounters a schema with up to 100,000 objects, it will paginate the results from `show objects` at 10,000 per page for up to 10 pages. + +Environments with more than 100,000 objects in a schema can customize the number of results per page and the page limit using the following [flags](/reference/global-configs/about-global-configs) in the `dbt_project.yml`: + +- `list_relations_per_page` — The number of relations on each page (Max 10k as this is the most Snowflake allows). +- `list_relations_page_limit` — The maximum number of pages to include in the results. + +For example, if you wanted to include 10,000 objects per page and include up to 100 pages (1 million objects), configure the flags as follows: + + +```yml + +flags: + list_relations_per_page: 10000 + list_relations_page_limit: 100 + +``` + + diff --git a/website/docs/reference/resource-configs/spark-configs.md b/website/docs/reference/resource-configs/spark-configs.md index 3b2174b8ff5..a52fd93eace 100644 --- a/website/docs/reference/resource-configs/spark-configs.md +++ b/website/docs/reference/resource-configs/spark-configs.md @@ -37,7 +37,8 @@ For that reason, the dbt-spark plugin leans heavily on the [`incremental_strateg - **`append`** (default): Insert new records without updating or overwriting any existing data. - **`insert_overwrite`**: If `partition_by` is specified, overwrite partitions in the with new data. If no `partition_by` is specified, overwrite the entire table with new data. - **`merge`** (Delta, Iceberg and Hudi file format only): Match records based on a `unique_key`; update old records, insert new ones. (If no `unique_key` is specified, all new data is inserted, similar to `append`.) - +- `microbatch` Implements the [microbatch strategy](/docs/build/incremental-microbatch) using `event_time` to define time-based ranges for filtering data. + Each of these strategies has its pros and cons, which we'll discuss below. As with any model config, `incremental_strategy` may be specified in `dbt_project.yml` or within a model file's `config()` block. ### The `append` strategy diff --git a/website/docs/reference/resource-configs/target_database.md b/website/docs/reference/resource-configs/target_database.md index 3c07b442107..f80dd31f214 100644 --- a/website/docs/reference/resource-configs/target_database.md +++ b/website/docs/reference/resource-configs/target_database.md @@ -6,7 +6,9 @@ datatype: string :::note -For [versionless](/docs/dbt-versions/core-upgrade/upgrading-to-v1.8#versionless) dbt Cloud accounts and dbt Core v1.9+, this functionality is no longer utilized. Use the [database](/reference/resource-configs/database) config as an alternative to define a custom database while still respecting the `generate_database_name` macro. +Starting in dbt Core v1.9+, this functionality is no longer utilized. Use the [database](/reference/resource-configs/database) config as an alternative to define a custom database while still respecting the `generate_database_name` macro. + +Try it now in the [dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks). ::: diff --git a/website/docs/reference/resource-configs/target_schema.md b/website/docs/reference/resource-configs/target_schema.md index ffa95df9be7..1117e3ec42c 100644 --- a/website/docs/reference/resource-configs/target_schema.md +++ b/website/docs/reference/resource-configs/target_schema.md @@ -6,7 +6,9 @@ datatype: string :::info -For [versionless](/docs/dbt-versions/core-upgrade/upgrading-to-v1.8#versionless) dbt Cloud accounts and dbt Core v1.9+, this configuration is no longer required. Use the [schema](/reference/resource-configs/schema) config as an alternative to define a custom schema while still respecting the `generate_schema_name` macro. +Starting in dbt Core v1.9+, this functionality is no longer utilized. Use the [database](/reference/resource-configs/database) config as an alternative to define a custom database while still respecting the `generate_database_name` macro. + +Try it now in the [dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks). ::: @@ -40,7 +42,7 @@ On **BigQuery**, this is analogous to a `dataset`. ## Default This is a required parameter, no default is provided. -For versionless dbt Cloud accounts and dbt Core v1.9+, this is not a required parameter. +In dbt Core v1.9+ and dbt Cloud "Latest" release track, this is not a required parameter. ## Examples ### Build all snapshots in a schema named `snapshots` diff --git a/website/docs/reference/resource-configs/unique_key.md b/website/docs/reference/resource-configs/unique_key.md index 996e7148292..77c99937295 100644 --- a/website/docs/reference/resource-configs/unique_key.md +++ b/website/docs/reference/resource-configs/unique_key.md @@ -1,6 +1,6 @@ --- resource_types: [snapshots] -description: "Unique_key - Read this in-depth guide to learn about configurations in dbt." +description: "Learn more about unique_key configurations in dbt." datatype: column_name_or_expression --- @@ -14,7 +14,7 @@ snapshots: - name: orders_snapshot relation: source('my_source', 'my_table') [config](/reference/snapshot-configs): - unique_key: id + unique_key: order_id ``` @@ -52,7 +52,7 @@ snapshots: ## Description A column name or expression that is unique for the inputs of a snapshot. dbt uses this to match records between a result set and an existing snapshot, so that changes can be captured correctly. -In Versionless and dbt v1.9 and later, [snapshots](/docs/build/snapshots) are defined and configured in YAML files within your `snapshots/` directory. The `unique_key` is specified within the `config` block of your snapshot YAML file. +In dbt Cloud "Latest" and dbt v1.9+, [snapshots](/docs/build/snapshots) are defined and configured in YAML files within your `snapshots/` directory. You can specify one or multiple `unique_key` values within your snapshot YAML file's `config` key. :::caution @@ -114,29 +114,37 @@ snapshots: -### Use a combination of two columns as a unique key -This configuration accepts a valid column expression. As such, you can concatenate two columns together as a unique key if required. It's a good idea to use a separator (e.g. `'-'`) to ensure uniqueness. - +### Use multiple unique keys + +You can configure snapshots to use multiple unique keys for `primary_key` columns. + ```yaml snapshots: - - name: transaction_items_snapshot - relation: source('erp', 'transactions') + - name: orders_snapshot + relation: source('jaffle_shop', 'orders') config: schema: snapshots - unique_key: "transaction_id || '-' || line_item_id" + unique_key: + - order_id + - product_id strategy: timestamp updated_at: updated_at - + ``` + +### Use a combination of two columns as a unique key + +This configuration accepts a valid column expression. As such, you can concatenate two columns together as a unique key if required. It's a good idea to use a separator (for example, `'-'`) to ensure uniqueness. + ```jinja2 @@ -159,25 +167,9 @@ from {{ source('erp', 'transactions') }} ``` - Though, it's probably a better idea to construct this column in your query and use that as the `unique_key`: - - - - -```yaml -snapshots: - - name: transaction_items_snapshot - relation: {{ ref('transaction_items_ephemeral') }} - config: - schema: snapshots - unique_key: id - strategy: timestamp - updated_at: updated_at -``` - @@ -195,9 +187,6 @@ from {{ source('erp', 'transactions') }} In this example, we create an ephemeral model `transaction_items_ephemeral` that creates an `id` column that can be used as the `unique_key` our snapshot configuration. - - - ```jinja2 diff --git a/website/docs/reference/resource-properties/concurrent_batches.md b/website/docs/reference/resource-properties/concurrent_batches.md new file mode 100644 index 00000000000..4d6b2ea0af4 --- /dev/null +++ b/website/docs/reference/resource-properties/concurrent_batches.md @@ -0,0 +1,90 @@ +--- +title: "concurrent_batches" +resource_types: [models] +datatype: model_name +description: "Learn about concurrent_batches in dbt." +--- + +:::note + +Available in dbt Core v1.9+ or the [dbt Cloud "Latest" release tracks](/docs/dbt-versions/cloud-release-tracks). + +::: + + + + + + + +```yaml +models: + +concurrent_batches: true +``` + + + + + + + + + + +```sql +{{ + config( + materialized='incremental', + concurrent_batches=true, + incremental_strategy='microbatch' + ... + ) +}} +select ... +``` + + + + + + +## Definition + +`concurrent_batches` is an override which allows you to decide whether or not you want to run batches in parallel or sequentially (one at a time). + +For more information, refer to [how batch execution works](/docs/build/incremental-microbatch#how-parallel-batch-execution-works). +## Example + +By default, dbt auto-detects whether batches can run in parallel for microbatch models. However, you can override dbt's detection by setting the `concurrent_batches` config to `false` in your `dbt_project.yml` or model `.sql` file to specify parallel or sequential execution, given you meet these conditions: +* You've configured a microbatch incremental strategy. +* You're working with cumulative metrics or any logic that depends on batch order. + +Set `concurrent_batches` config to `false` to ensure batches are processed sequentially. For example: + + + +```yaml +models: + my_project: + cumulative_metrics_model: + +concurrent_batches: false +``` + + + + + +```sql +{{ + config( + materialized='incremental', + incremental_strategy='microbatch' + concurrent_batches=false + ) +}} +select ... + +``` + + + diff --git a/website/docs/reference/resource-properties/constraints.md b/website/docs/reference/resource-properties/constraints.md index 63582974040..1e418e884be 100644 --- a/website/docs/reference/resource-properties/constraints.md +++ b/website/docs/reference/resource-properties/constraints.md @@ -29,7 +29,7 @@ Foreign key constraints accept two additional inputs: - `to`: A relation input, likely `ref()`, indicating the referenced table. - `to_columns`: A list of column(s) in that table containing the corresponding primary or unique key. -This syntax for defining foreign keys uses `ref`, meaning it will capture dependencies and works across different environments. It's available in [dbt Cloud Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) and versions of dbt Core starting with v1.9. +This syntax for defining foreign keys uses `ref`, meaning it will capture dependencies and works across different environments. It's available in [dbt Cloud "Latest""](/docs/dbt-versions/cloud-release-tracks) and [dbt Core v1.9+](/docs/dbt-versions/core-upgrade/upgrading-to-v1.9). @@ -65,7 +65,7 @@ models: - type: unique - type: foreign_key to: ref('other_model_name') - to_columns: other_model_column + to_columns: [other_model_column] - type: ... ``` diff --git a/website/docs/reference/resource-properties/schema.md b/website/docs/reference/resource-properties/schema.md index 017d93e3235..6b5ba66ff8f 100644 --- a/website/docs/reference/resource-properties/schema.md +++ b/website/docs/reference/resource-properties/schema.md @@ -10,7 +10,7 @@ datatype: schema_name ```yml version: 2 -sources: +[sources](/reference/source-properties): - name: database: schema: @@ -25,7 +25,7 @@ sources: ## Definition The schema name as stored in the database. -This parameter is useful if you want to use a source name that differs from the schema name. +This parameter is useful if you want to use a [source](/reference/source-properties) name that differs from the schema name. :::info BigQuery terminology diff --git a/website/docs/reference/resource-properties/unit-tests.md b/website/docs/reference/resource-properties/unit-tests.md index 08081c4c24a..7bc177a133c 100644 --- a/website/docs/reference/resource-properties/unit-tests.md +++ b/website/docs/reference/resource-properties/unit-tests.md @@ -7,7 +7,7 @@ datatype: test :::note -This functionality is only supported in dbt Core v1.8+ or dbt Cloud accounts that have gone ["Versionless"](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless). +This functionality is available in dbt Core v1.8+ and [dbt Cloud release tracks](/docs/dbt-versions/cloud-release-tracks). ::: diff --git a/website/docs/reference/resource-properties/versions.md b/website/docs/reference/resource-properties/versions.md index f6b71852aef..748aa477a4f 100644 --- a/website/docs/reference/resource-properties/versions.md +++ b/website/docs/reference/resource-properties/versions.md @@ -73,13 +73,13 @@ Note that the value of `defined_in` and the `alias` configuration of a model are When you use the `state:modified` selection method in Slim CI, dbt will detect changes to versioned model contracts, and raise an error if any of those changes could be breaking for downstream consumers. -Breaking changes include: -- Removing an existing column -- Changing the `data_type` of an existing column -- Removing or modifying one of the `constraints` on an existing column (dbt v1.6 or higher) -- Changing unversioned, contracted models. - - dbt also warns if a model has or had a contract but isn't versioned - +import BreakingChanges from '/snippets/_versions-contracts.md'; + + + diff --git a/website/docs/reference/seed-configs.md b/website/docs/reference/seed-configs.md index 5d5c39071d6..a18f1fc28f7 100644 --- a/website/docs/reference/seed-configs.md +++ b/website/docs/reference/seed-configs.md @@ -79,6 +79,8 @@ seeds: + + ```yaml seeds: [](/reference/resource-configs/resource-path): @@ -95,7 +97,28 @@ seeds: [+](/reference/resource-configs/plus-prefix)[grants](/reference/resource-configs/grants): {} ``` + + + + +```yaml +seeds: + [](/reference/resource-configs/resource-path): + [+](/reference/resource-configs/plus-prefix)[enabled](/reference/resource-configs/enabled): true | false + [+](/reference/resource-configs/plus-prefix)[tags](/reference/resource-configs/tags): | [] + [+](/reference/resource-configs/plus-prefix)[pre-hook](/reference/resource-configs/pre-hook-post-hook): | [] + [+](/reference/resource-configs/plus-prefix)[post-hook](/reference/resource-configs/pre-hook-post-hook): | [] + [+](/reference/resource-configs/plus-prefix)[database](/reference/resource-configs/database): + [+](/reference/resource-configs/plus-prefix)[schema](/reference/resource-properties/schema): + [+](/reference/resource-configs/plus-prefix)[alias](/reference/resource-configs/alias): + [+](/reference/resource-configs/plus-prefix)[persist_docs](/reference/resource-configs/persist_docs): + [+](/reference/resource-configs/plus-prefix)[full_refresh](/reference/resource-configs/full_refresh): + [+](/reference/resource-configs/plus-prefix)[meta](/reference/resource-configs/meta): {} + [+](/reference/resource-configs/plus-prefix)[grants](/reference/resource-configs/grants): {} + [+](/reference/resource-configs/plus-prefix)[event_time](/reference/resource-configs/event-time): my_time_field +``` + @@ -105,6 +128,8 @@ seeds: + + ```yaml version: 2 @@ -122,13 +147,36 @@ seeds: [full_refresh](/reference/resource-configs/full_refresh): [meta](/reference/resource-configs/meta): {} [grants](/reference/resource-configs/grants): {} + [event_time](/reference/resource-configs/event-time): my_time_field + +``` + + + + +```yaml +version: 2 +seeds: + - name: [] + config: + [enabled](/reference/resource-configs/enabled): true | false + [tags](/reference/resource-configs/tags): | [] + [pre_hook](/reference/resource-configs/pre-hook-post-hook): | [] + [post_hook](/reference/resource-configs/pre-hook-post-hook): | [] + [database](/reference/resource-configs/database): + [schema](/reference/resource-properties/schema): + [alias](/reference/resource-configs/alias): + [persist_docs](/reference/resource-configs/persist_docs): + [full_refresh](/reference/resource-configs/full_refresh): + [meta](/reference/resource-configs/meta): {} + [grants](/reference/resource-configs/grants): {} ``` + - ## Configuring seeds diff --git a/website/docs/reference/snapshot-configs.md b/website/docs/reference/snapshot-configs.md index 144ecafde9d..018988a4934 100644 --- a/website/docs/reference/snapshot-configs.md +++ b/website/docs/reference/snapshot-configs.md @@ -8,30 +8,16 @@ meta: import ConfigResource from '/snippets/_config-description-resource.md'; import ConfigGeneral from '/snippets/_config-description-general.md'; - ## Related documentation * [Snapshots](/docs/build/snapshots) * The `dbt snapshot` [command](/reference/commands/snapshot) - ## Available configurations ### Snapshot-specific configurations - - -import SnapshotYaml from '/snippets/_snapshot-yaml-spec.md'; - - - - - [+](/reference/resource-configs/plus-prefix)[check_cols](/reference/resource-configs/check_cols): [] | all [+](/reference/resource-configs/plus-prefix)[snapshot_meta_column_names](/reference/resource-configs/snapshot_meta_column_names): {} - [+](/reference/resource-configs/plus-prefix)[invalidate_hard_deletes](/reference/resource-configs/invalidate_hard_deletes) : true | false + [+](/reference/resource-configs/plus-prefix)[dbt_valid_to_current](/reference/resource-configs/dbt_valid_to_current): + [+](/reference/resource-configs/plus-prefix)[hard_deletes](/reference/resource-configs/hard-deletes): string ``` @@ -113,7 +100,8 @@ snapshots: [updated_at](/reference/resource-configs/updated_at): [check_cols](/reference/resource-configs/check_cols): [] | all [snapshot_meta_column_names](/reference/resource-configs/snapshot_meta_column_names): {} - [invalidate_hard_deletes](/reference/resource-configs/invalidate_hard_deletes) : true | false + [hard_deletes](/reference/resource-configs/hard-deletes): string + [dbt_valid_to_current](/reference/resource-configs/dbt_valid_to_current): ``` @@ -123,11 +111,9 @@ snapshots: - - -Configurations can be applied to snapshots using the [YAML syntax](/docs/build/snapshots), available in Versionless and dbt v1.9 and higher, in the `snapshot` directory file. +import LegacySnapshotConfig from '/snippets/_legacy-snapshot-config.md'; - + @@ -150,11 +136,25 @@ Configurations can be applied to snapshots using the [YAML syntax](/docs/build/s +### Snapshot configuration migration + +The latest snapshot configurations introduced in dbt Core v1.9 (such as [`snapshot_meta_column_names`](/reference/resource-configs/snapshot_meta_column_names), [`dbt_valid_to_current`](/reference/resource-configs/dbt_valid_to_current), and `hard_deletes`) are best suited for new snapshots. For existing snapshots, we recommend the following to avoid any inconsistencies in your snapshots: + +#### For existing snapshots +- Migrate tables — Migrate the previous snapshot to the new table schema and values: + - Create a backup copy of your snapshots. + - Use `alter` statements as needed (or a script to apply `alter` statements) to ensure table consistency. +- New configurations — Convert the configs one at a time, testing as you go. + +:::warning +If you use one of the latest configs, such as `dbt_valid_to_current`, without migrating your data, you may have mixed old and new data, leading to an incorrect downstream result. +::: ### General configurations + + + + ```yaml snapshots: [](/reference/resource-configs/resource-path): @@ -178,7 +181,24 @@ snapshots: [+](/reference/resource-configs/plus-prefix)[post-hook](/reference/resource-configs/pre-hook-post-hook): | [] [+](/reference/resource-configs/plus-prefix)[persist_docs](/reference/resource-configs/persist_docs): {} [+](/reference/resource-configs/plus-prefix)[grants](/reference/resource-configs/grants): {} + [+](/reference/resource-configs/plus-prefix)[event_time](/reference/resource-configs/event-time): my_time_field ``` + + + + +```yaml +snapshots: + [](/reference/resource-configs/resource-path): + [+](/reference/resource-configs/plus-prefix)[enabled](/reference/resource-configs/enabled): true | false + [+](/reference/resource-configs/plus-prefix)[tags](/reference/resource-configs/tags): | [] + [+](/reference/resource-configs/plus-prefix)[alias](/reference/resource-configs/alias): + [+](/reference/resource-configs/plus-prefix)[pre-hook](/reference/resource-configs/pre-hook-post-hook): | [] + [+](/reference/resource-configs/plus-prefix)[post-hook](/reference/resource-configs/pre-hook-post-hook): | [] + [+](/reference/resource-configs/plus-prefix)[persist_docs](/reference/resource-configs/persist_docs): {} + [+](/reference/resource-configs/plus-prefix)[grants](/reference/resource-configs/grants): {} +``` + @@ -198,8 +218,8 @@ snapshots: [enabled](/reference/resource-configs/enabled): true | false [tags](/reference/resource-configs/tags): | [] [alias](/reference/resource-configs/alias): - [pre-hook](/reference/resource-configs/pre-hook-post-hook): | [] - [post-hook](/reference/resource-configs/pre-hook-post-hook): | [] + [pre_hook](/reference/resource-configs/pre-hook-post-hook): | [] + [post_hook](/reference/resource-configs/pre-hook-post-hook): | [] [persist_docs](/reference/resource-configs/persist_docs): {} [grants](/reference/resource-configs/grants): {} ``` @@ -221,10 +241,11 @@ snapshots: [enabled](/reference/resource-configs/enabled): true | false [tags](/reference/resource-configs/tags): | [] [alias](/reference/resource-configs/alias): - [pre-hook](/reference/resource-configs/pre-hook-post-hook): | [] - [post-hook](/reference/resource-configs/pre-hook-post-hook): | [] + [pre_hook](/reference/resource-configs/pre-hook-post-hook): | [] + [post_hook](/reference/resource-configs/pre-hook-post-hook): | [] [persist_docs](/reference/resource-configs/persist_docs): {} [grants](/reference/resource-configs/grants): {} + [event_time](/reference/resource-configs/event-time): my_time_field ``` @@ -234,11 +255,7 @@ snapshots: - - -Configurations can be applied to snapshots using [YAML syntax](/docs/build/snapshots), available in Versionless and dbt v1.9 and higher, in the `snapshot` directory file. - - + @@ -267,24 +284,29 @@ Snapshots can be configured in multiple ways: -1. Defined in YAML files using a `config` [resource property](/reference/model-properties), typically in your [snapshots directory](/reference/project-configs/snapshot-paths) (available in [Versionless](/docs/dbt-versions/versionless-cloud) or and dbt Core v1.9 and higher). +1. Defined in YAML files using a `config` [resource property](/reference/model-properties), typically in your [snapshots directory](/reference/project-configs/snapshot-paths) (available in [the dbt Cloud release track](/docs/dbt-versions/cloud-release-tracks) and dbt v1.9 and higher). 2. From the `dbt_project.yml` file, under the `snapshots:` key. To apply a configuration to a snapshot, or directory of snapshots, define the resource path as nested dictionary keys. -1. Defined in YAML files using a `config` [resource property](/reference/model-properties), typically in your [snapshots directory](/reference/project-configs/snapshot-paths) (available in [Versionless](/docs/dbt-versions/versionless-cloud) or and dbt Core v1.9 and higher). -2. Using a `config` block within a snapshot defined in Jinja SQL +1. Defined in a YAML file using a `config` [resource property](/reference/model-properties), typically in your [snapshots directory](/reference/project-configs/snapshot-paths) (available in [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) and dbt v1.9 and higher). The latest snapshot YAML syntax provides faster and more efficient management. +2. Using a `config` block within a snapshot defined in Jinja SQL. 3. From the `dbt_project.yml` file, under the `snapshots:` key. To apply a configuration to a snapshot, or directory of snapshots, define the resource path as nested dictionary keys. -Note that in Versionless and dbt v1.9 and later, snapshots are defined in an updated syntax using a YAML file within your `snapshots/` directory (as defined by the [`snapshot-paths` config](/reference/project-configs/snapshot-paths)). For faster and more efficient management, consider the updated snapshot YAML syntax, [available in Versionless](/docs/dbt-versions/versionless-cloud) or [dbt Core v1.9 and later](/docs/dbt-versions/core). - Snapshot configurations are applied hierarchically in the order above with higher taking precedence. ### Examples -The following examples demonstrate how to configure snapshots using the `dbt_project.yml` file, a `config` block within a snapshot, and a `.yml` file. + + +The following examples demonstrate how to configure snapshots using the `dbt_project.yml` file and a `.yml` file. + + + +The following examples demonstrate how to configure snapshots using the `dbt_project.yml` file, a `config` block within a snapshot (legacy method), and a `.yml` file. + - #### Apply configurations to all snapshots To apply a configuration to all snapshots, including those in any installed [packages](/docs/build/packages), nest the configuration directly under the `snapshots` key: @@ -292,7 +314,6 @@ The following examples demonstrate how to configure snapshots using the `dbt_pro ```yml - snapshots: +unique_key: id ``` @@ -307,7 +328,6 @@ The following examples demonstrate how to configure snapshots using the `dbt_pro ```yml - snapshots: jaffle_shop: +unique_key: id @@ -329,6 +349,7 @@ The following examples demonstrate how to configure snapshots using the `dbt_pro {{ config( unique_key='id', + target_schema='snapshots', strategy='timestamp', updated_at='updated_at' ) @@ -378,7 +399,7 @@ The following examples demonstrate how to configure snapshots using the `dbt_pro - You can also define some common configs in a snapshot's `config` block. We don't recommend this for a snapshot's required configuration, however. + You can also define some common configs in a snapshot's `config` block. However, we don't recommend this for a snapshot's required configuration. diff --git a/website/docs/reference/snapshot-properties.md b/website/docs/reference/snapshot-properties.md index d940a9f344c..11fb956a163 100644 --- a/website/docs/reference/snapshot-properties.md +++ b/website/docs/reference/snapshot-properties.md @@ -5,7 +5,7 @@ description: "Read this guide to learn about using source properties in dbt." -In Versionless and dbt v1.9 and later, snapshots are defined and configured in YAML files within your `snapshots/` directory (as defined by the [`snapshot-paths` config](/reference/project-configs/snapshot-paths)). Snapshot properties are declared within these YAML files, allowing you to define both the snapshot configurations and properties in one place. +In dbt v1.9 and later, snapshots are defined and configured in YAML files within your `snapshots/` directory (as defined by the [`snapshot-paths` config](/reference/project-configs/snapshot-paths)). Snapshot properties are declared within these YAML files, allowing you to define both the snapshot configurations and properties in one place. @@ -15,7 +15,7 @@ Snapshots properties can be declared in `.yml` files in: - your `snapshots/` directory (as defined by the [`snapshot-paths` config](/reference/project-configs/snapshot-paths)). - your `models/` directory (as defined by the [`model-paths` config](/reference/project-configs/model-paths)) -Note, in Versionless and dbt v1.9 and later, snapshots are defined in an updated syntax using a YAML file within your `snapshots/` directory (as defined by the [`snapshot-paths` config](/reference/project-configs/snapshot-paths)). For faster and more efficient management, consider the updated snapshot YAML syntax, [available in Versionless](/docs/dbt-versions/versionless-cloud) or [dbt Core v1.9 and later](/docs/dbt-versions/core). +Note, in dbt v1.9 and later, snapshots are defined in an updated syntax using a YAML file within your `snapshots/` directory (as defined by the [`snapshot-paths` config](/reference/project-configs/snapshot-paths)). For faster and more efficient management, consider the updated snapshot YAML syntax, available now in [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) and soon in [dbt Core v1.9](/docs/dbt-versions/core-upgrade/upgrading-to-v1.9). diff --git a/website/docs/reference/source-configs.md b/website/docs/reference/source-configs.md index 64dda8bffde..959d4c542e9 100644 --- a/website/docs/reference/source-configs.md +++ b/website/docs/reference/source-configs.md @@ -8,7 +8,17 @@ import ConfigGeneral from '/snippets/_config-description-general.md'; ## Available configurations -Sources only support one configuration, [`enabled`](/reference/resource-configs/enabled). + + +Sources supports [`enabled`](/reference/resource-configs/enabled) and [`meta`](/reference/resource-configs/meta). + + + + + +Sources configurations support [`enabled`](/reference/resource-configs/enabled), [`event_time`](/reference/resource-configs/event-time), and [`meta`](/reference/resource-configs/meta) + + ### General configurations @@ -27,12 +37,29 @@ Sources only support one configuration, [`enabled`](/reference/resource-configs/ + + ```yaml sources: [](/reference/resource-configs/resource-path): [+](/reference/resource-configs/plus-prefix)[enabled](/reference/resource-configs/enabled): true | false + [+](/reference/resource-configs/plus-prefix)[event_time](/reference/resource-configs/event-time): my_time_field + [+](/reference/resource-configs/plus-prefix)[meta](/reference/resource-configs/meta): + key: value ``` + + + + +```yaml +sources: + [](/reference/resource-configs/resource-path): + [+](/reference/resource-configs/plus-prefix)[enabled](/reference/resource-configs/enabled): true | false + [+](/reference/resource-configs/plus-prefix)[meta](/reference/resource-configs/meta): + key: value +``` + @@ -43,6 +70,8 @@ sources: + + ```yaml version: 2 @@ -50,12 +79,37 @@ sources: - name: [] [config](/reference/resource-properties/config): [enabled](/reference/resource-configs/enabled): true | false + [event_time](/reference/resource-configs/event-time): my_time_field + [meta](/reference/resource-configs/meta): {} + tables: - name: [] [config](/reference/resource-properties/config): [enabled](/reference/resource-configs/enabled): true | false + [event_time](/reference/resource-configs/event-time): my_time_field + [meta](/reference/resource-configs/meta): {} ``` + + + + +```yaml +version: 2 + +sources: + - name: [] + [config](/reference/resource-properties/config): + [enabled](/reference/resource-configs/enabled): true | false + [meta](/reference/resource-configs/meta): {} + tables: + - name: [] + [config](/reference/resource-properties/config): + [enabled](/reference/resource-configs/enabled): true | false + [meta](/reference/resource-configs/meta): {} + +``` + @@ -74,6 +128,8 @@ You can disable sources imported from a package to prevent them from rendering i + + ```yaml sources: your_project_name: @@ -81,11 +137,34 @@ You can disable sources imported from a package to prevent them from rendering i source_name: source_table_name: +enabled: false + +event_time: my_time_field ``` + + + + + ```yaml + sources: + your_project_name: + subdirectory_name: + source_name: + source_table_name: + +enabled: false + ``` + ### Examples + +The following examples show how to configure sources in your dbt project. + +— [Disable all sources imported from a package](#disable-all-sources-imported-from-a-package)
+— [Conditionally enable a single source](#conditionally-enable-a-single-source)
+— [Disable a single source from a package](#disable-a-single-source-from-a-package)
+— [Configure a source with an `event_time`](#configure-a-source-with-an-event_time)
+— [Configure meta to a source](#configure-meta-to-a-source)
+ #### Disable all sources imported from a package To apply a configuration to all sources included from a [package](/docs/build/packages), state your configuration under the [project name](/reference/project-configs/name.md) in the @@ -172,6 +251,53 @@ sources: +#### Configure a source with an `event_time` + + + +Configuring an [`event_time`](/reference/resource-configs/event-time) for a source is only available in [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) or dbt Core versions 1.9 and later. + + + + + +To configure a source with an `event_time`, specify the `event_time` field in the source configuration. This field is used to represent the actual timestamp of the event, rather than something like a loading date. + +For example, if you had a source table called `clickstream` in the `events` source, you can use the timestamp for each event in the `event_timestamp` column as follows: + + + +```yaml +sources: + events: + clickstream: + +event_time: event_timestamp +``` + + +In this example, the `event_time` is set to `event_timestamp`, which has the exact time each clickstream event happened. +Not only is this required for the [incremental microbatching strategy](/docs/build/incremental-microbatch), but when you compare data across [CI and production](/docs/deploy/advanced-ci#speeding-up-comparisons) environments, dbt will use `event_timestamp` to filter and match data by this event-based timeframe, ensuring that only overlapping timeframes are compared. + + + +#### Configure meta to a source + +Use the `meta` field to assign metadata information to sources. This is useful for tracking additional context, documentation, logging, and more. + +For example, you can add `meta` information to a `clickstream` source to include information about the data source system: + + + +```yaml +sources: + events: + clickstream: + +meta: + source_system: "Google analytics" + data_owner: "marketing_team" +``` + + ## Example source configuration The following is a valid source configuration for a project with: * `name: jaffle_shop` diff --git a/website/package-lock.json b/website/package-lock.json index 5fdf491f1cf..8d573ee3426 100644 --- a/website/package-lock.json +++ b/website/package-lock.json @@ -13,9 +13,9 @@ "@docusaurus/theme-search-algolia": "3.4.0", "@mdx-js/react": "^3.0.1", "@monaco-editor/react": "^4.4.6", - "@stoplight/elements": "^7.7.17", + "@stoplight/elements": "^7.5.8", "@svgr/webpack": "^6.0.0", - "axios": "^0.27.2", + "axios": "^1.7.7", "canvas-confetti": "^1.9.2", "classnames": "^2.3.1", "clsx": "^1.1.1", @@ -27,12 +27,12 @@ "gray-matter": "^4.0.3", "hast-util-is-element": "^1.1.0", "js-yaml": "^4.1.0", + "markdown-to-jsx": "^7.5.0", "mobx": "^6.3.9", "node-polyfill-webpack-plugin": "^1.1.4", "papaparse": "^5.3.2", "prism-react-renderer": "^2.3.1", "query-string": "^8.1.0", - "raw-loader": "^4.0.2", "react": "^18.2.0", "react-dom": "^18.2.0", "react-full-screen": "^1.1.1", @@ -40,7 +40,7 @@ "react-select": "^5.7.5", "react-tooltip": "^4.2.21", "redoc": "^2.0.0-rc.57", - "rehype-katex": "^5.0.0", + "rehype-katex": "^7.0.1", "remark-math": "^3.0.1", "sanitize-html": "^2.8.0", "slugify": "^1.6.1", @@ -57,7 +57,7 @@ "@testing-library/user-event": "^14.5.2", "@typescript-eslint/eslint-plugin": "^5.54.0", "@typescript-eslint/parser": "^5.54.0", - "css-loader": "^3.4.2", + "css-loader": "^7.1.2", "cypress": "^13.11.0", "dotenv": "^10.0.0", "eslint": "^8.35.0", @@ -70,6 +70,7 @@ "lint-staged": "^13.1.2", "path-browserify": "^1.0.1", "process": "^0.11.10", + "raw-loader": "^4.0.2", "stream-http": "^3.2.0", 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"integrity": "sha512-amQa0Ep2m6hE2g72AugUItjbuM8X8cGQnFoHk0pGfrFeT9GZhzN5SW8nRsiGKK7Aif4CrACPENkA6P/Lw6fHGQ==", "dependencies": { - "unist-util-is": "^4.0.0" + "@types/unist": "^3.0.0", + "unist-util-is": "^6.0.0" }, "funding": { "type": "opencollective", "url": "https://opencollective.com/unified" } }, - "node_modules/unist-util-find-after/node_modules/unist-util-is": { - "version": "4.1.0", - "resolved": "https://registry.npmjs.org/unist-util-is/-/unist-util-is-4.1.0.tgz", - "integrity": "sha512-ZOQSsnce92GrxSqlnEEseX0gi7GH9zTJZ0p9dtu87WRb/37mMPO2Ilx1s/t9vBHrFhbgweUwb+t7cIn5dxPhZg==", - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/unified" - } - }, "node_modules/unist-util-generated": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/unist-util-generated/-/unist-util-generated-2.0.1.tgz", @@ -28403,6 +27971,19 @@ "resolved": "https://registry.npmjs.org/@types/unist/-/unist-2.0.11.tgz", "integrity": "sha512-CmBKiL6NNo/OqgmMn95Fk9Whlp2mtvIv+KNpQKN2F4SjvrEesubTRWGYSg+BnWZOnlCaSTU1sMpsBOzgbYhnsA==" }, + "node_modules/unist-util-remove-position": { + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/unist-util-remove-position/-/unist-util-remove-position-5.0.0.tgz", + "integrity": "sha512-Hp5Kh3wLxv0PHj9m2yZhhLt58KzPtEYKQQ4yxfYFEO7EvHwzyDYnduhHnY1mDxoqr7VUwVuHXk9RXKIiYS1N8Q==", + "dependencies": { + "@types/unist": "^3.0.0", + "unist-util-visit": "^5.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, "node_modules/unist-util-select": { "version": "4.0.3", "resolved": "https://registry.npmjs.org/unist-util-select/-/unist-util-select-4.0.3.tgz", diff --git a/website/package.json b/website/package.json index 51edae4bf17..73cca4c63e3 100644 --- a/website/package.json +++ b/website/package.json @@ -16,9 +16,9 @@ "@docusaurus/theme-search-algolia": "3.4.0", "@mdx-js/react": "^3.0.1", "@monaco-editor/react": "^4.4.6", - "@stoplight/elements": "^7.7.17", + "@stoplight/elements": "^7.5.8", "@svgr/webpack": "^6.0.0", - "axios": "^0.27.2", + "axios": "^1.7.7", "canvas-confetti": "^1.9.2", "classnames": "^2.3.1", "clsx": "^1.1.1", @@ -30,12 +30,12 @@ "gray-matter": "^4.0.3", "hast-util-is-element": "^1.1.0", "js-yaml": "^4.1.0", + "markdown-to-jsx": "^7.5.0", "mobx": "^6.3.9", "node-polyfill-webpack-plugin": "^1.1.4", "papaparse": "^5.3.2", "prism-react-renderer": "^2.3.1", "query-string": "^8.1.0", - "raw-loader": "^4.0.2", "react": "^18.2.0", "react-dom": "^18.2.0", "react-full-screen": "^1.1.1", @@ -43,7 +43,7 @@ "react-select": "^5.7.5", "react-tooltip": "^4.2.21", "redoc": "^2.0.0-rc.57", - "rehype-katex": "^5.0.0", + "rehype-katex": "^7.0.1", "remark-math": "^3.0.1", "sanitize-html": "^2.8.0", "slugify": "^1.6.1", @@ -63,7 +63,7 @@ "@testing-library/user-event": "^14.5.2", "@typescript-eslint/eslint-plugin": "^5.54.0", "@typescript-eslint/parser": "^5.54.0", - "css-loader": "^3.4.2", + "css-loader": "^7.1.2", "cypress": "^13.11.0", "dotenv": "^10.0.0", "eslint": "^8.35.0", @@ -76,6 +76,7 @@ "lint-staged": "^13.1.2", "path-browserify": "^1.0.1", "process": "^0.11.10", + "raw-loader": "^4.0.2", "stream-http": "^3.2.0", "style-loader": "^1.1.3", "svg-inline-loader": "^0.8.2", diff --git a/website/sidebars.js b/website/sidebars.js index 114c40cb09f..08494e4c713 100644 --- a/website/sidebars.js +++ b/website/sidebars.js @@ -49,6 +49,7 @@ const sidebarSettings = { items: [ "docs/cloud/about-cloud-setup", "docs/cloud/account-settings", + "docs/cloud/account-integrations", "docs/dbt-cloud-environments", "docs/cloud/migration", { @@ -302,9 +303,9 @@ const sidebarSettings = { }, ], }, - ], - }, - { + ], + }, + { type: "category", label: "Build dbt projects", collapsed: true, @@ -776,7 +777,7 @@ const sidebarSettings = { link: { type: "doc", id: "docs/dbt-versions/core" }, items: [ "docs/dbt-versions/core", - "docs/dbt-versions/versionless-cloud", + "docs/dbt-versions/cloud-release-tracks", "docs/dbt-versions/upgrade-dbt-version-in-cloud", "docs/dbt-versions/product-lifecycles", "docs/dbt-versions/experimental-features", @@ -805,6 +806,7 @@ const sidebarSettings = { }, items: [ "docs/dbt-versions/dbt-cloud-release-notes", + "docs/dbt-versions/compatible-track-changelog", "docs/dbt-versions/2023-release-notes", "docs/dbt-versions/2022-release-notes", { @@ -924,13 +926,17 @@ const sidebarSettings = { items: [ "reference/resource-configs/access", "reference/resource-configs/alias", + "reference/resource-configs/batch-size", + "reference/resource-configs/begin", "reference/resource-configs/database", "reference/resource-configs/enabled", + "reference/resource-configs/event-time", "reference/resource-configs/full_refresh", "reference/resource-configs/contract", "reference/resource-configs/grants", "reference/resource-configs/group", "reference/resource-configs/docs", + "reference/resource-configs/lookback", "reference/resource-configs/persist_docs", "reference/resource-configs/pre-hook-post-hook", "reference/resource-configs/schema", @@ -950,6 +956,7 @@ const sidebarSettings = { "reference/resource-configs/materialized", "reference/resource-configs/on_configuration_change", "reference/resource-configs/sql_header", + "reference/resource-properties/concurrent_batches", ], }, { @@ -968,16 +975,18 @@ const sidebarSettings = { label: "For snapshots", items: [ "reference/snapshot-properties", - "reference/resource-configs/snapshot_name", "reference/snapshot-configs", "reference/resource-configs/check_cols", + "reference/resource-configs/dbt_valid_to_current", + "reference/resource-configs/hard-deletes", + "reference/resource-configs/invalidate_hard_deletes", + "reference/resource-configs/snapshot_meta_column_names", + "reference/resource-configs/snapshot_name", "reference/resource-configs/strategy", "reference/resource-configs/target_database", "reference/resource-configs/target_schema", "reference/resource-configs/unique_key", "reference/resource-configs/updated_at", - "reference/resource-configs/invalidate_hard_deletes", - "reference/resource-configs/snapshot_meta_column_names", ], }, { diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md index 6addd6a3a7a..6d202d01998 100644 --- a/website/snippets/_cloud-environments-info.md +++ b/website/snippets/_cloud-environments-info.md @@ -33,9 +33,7 @@ Both development and deployment environments have a section called **General Set :::note About dbt version -- dbt Cloud allows users to select any dbt release. At this time, **environments must use a dbt version greater than or equal to v1.0.0;** [lower versions are no longer supported](/docs/dbt-versions/upgrade-dbt-version-in-cloud). -- If you select a current version with `(latest)` in the name, your environment will automatically install the latest stable version of the minor version selected. -- Go **Versionless**, which removes the need for manually upgrading environment, while ensuring you are always up to date with the latest fixes and features. +dbt Cloud allows users to select a [release track](/docs/dbt-versions/cloud-release-tracks) to receive ongoing dbt version upgrades at the cadence that makes sense for their team. ::: ### Custom branch behavior diff --git a/website/snippets/_config-dbt-version-check.md b/website/snippets/_config-dbt-version-check.md index d4e495bd379..6dc2e702895 100644 --- a/website/snippets/_config-dbt-version-check.md +++ b/website/snippets/_config-dbt-version-check.md @@ -1,5 +1,5 @@ -Starting in 2024, when you select **Versionless** in dbt Cloud, dbt will ignore the `require-dbt-version` config. Refer to [Versionless](/docs/dbt-versions/upgrade-dbt-version-in-cloud#versionless) for more details. +Starting in 2024, when you select a [release track in dbt Cloud](/docs/dbt-versions/cloud-release-tracks) to receive ongoing dbt version upgrades, dbt will ignore the `require-dbt-version` config. dbt Labs is committed to zero breaking changes for code in dbt projects, with ongoing releases to dbt Cloud and new versions of dbt Core. We also recommend these best practices: diff --git a/website/snippets/_enterprise-permissions-table.md b/website/snippets/_enterprise-permissions-table.md index 688e8911bf4..b39337697c1 100644 --- a/website/snippets/_enterprise-permissions-table.md +++ b/website/snippets/_enterprise-permissions-table.md @@ -1,9 +1,4 @@ -Key: - -* (W)rite — Create new or modify existing. Includes `send`, `create`, `delete`, `allocate`, `modify`, `develop`, and `read`. -* (R)ead — Can view but can not create or change any fields. - Permissions: * Account-level permissions — Permissions related to the management of the dbt Cloud account. For example, billing and account settings. @@ -12,77 +7,114 @@ Permissions: ### Account roles Account roles enable you to manage the dbt Cloud account and manage the account settings (for example, generating service tokens, inviting users, and configuring SSO). They also provide project-level permissions. The **Account Admin** role is the highest level of access you can assign. +Key: + +* (W)rite — Create new or modify existing. Includes `send`, `create`, `delete`, `allocate`, `modify`, and `develop`. +* (R)ead — Can view but can not create or change any fields. + #### Account permissions for account roles -| Account-level permission| Account Admin | Billing admin | Manage

marketplace

apps | Project creator | Security admin | Viewer | + + + +{` +| Account-level permission| Account Admin | Billing admin | Manage marketplace apps | Project creator | Security admin | Viewer | |:-------------------------|:-------------:|:------------:|:-------------------------:|:---------------:|:--------------:|:------:| -| Account settings | W | | | R | R | R | -| Audit logs | R | | | | R | R | -| Auth provider | W | | | | W | R | -| Billing | W | W | | | | R | -| Connections | W | | | W | | | -| Groups | W | | | R | W | R | -| Invitations | W | | | W | W | R | -| IP restrictions | W | | | | W | R | -| Licenses | W | | | W | W | R | -| Marketplace app | | | W | | | | -| Members | W | | | W | W | R | -| Project (create) | W | | | W | | | -| Public models | R | R | | R | R | R | -| Service tokens | W | | | | R | R | -| Webhooks | W | | | | | | +| Account settings | W | - | - | R | R | R | +| Audit logs | R | - | - | - | R | R | +| Auth provider | W | - | - | - | W | R | +| Billing | W | W | - | - | - | R | +| Connections | W | - | - | W | - | - | +| Groups | W | - | - | R | W | R | +| Invitations | W | - | - | W | W | R | +| IP restrictions | W | - | - | - | W | R | +| Licenses | W | - | - | W | W | R | +| Marketplace app | - | - | W | - | - | - | +| Members | W | - | - | W | W | R | +| Project (create) | W | - | - | W | - | - | +| Public models | R | R | - | R | R | R | +| Service tokens | W | - | - | - | R | R | +| Webhooks | W | - | - | - | - | - | +`} + + #### Project permissions for account roles + + +{` |Project-level permission | Account Admin | Billing admin | Project creator | Security admin | Viewer | |:-------------------------|:-------------:|:-------------:|:---------------:|:--------------:|:------:| -| Environment credentials (deployment) | W | | W | | R | -| Custom env. variables | W | | W | | R | -| Data platform configurations | W | | W | | R | -| Develop (IDE or dbt Cloud CLI) | W | | W | | | -| Environments | W | | W | | R | -| Jobs | W | | W | | R | -| Metadata GraphQL API access | R | | R | | R | -| Permissions | W | | W | W | R | -| Projects | W | | W | R | R | -| Repositories | W | | W | | R | -| Runs | W | | W | | R | -| Semantic Layer config | W | | W | | R | +| Environment credentials | W | - | W | - | R | +| Custom env. variables | W | - | W | - | R | +| Data platform configurations| W | - | W | - | R | +| Develop (IDE or CLI) | W | - | W | - | - | +| Environments | W | - | W | - | R | +| Jobs | W | - | W | - | R | +| Metadata GraphQL API access | R | - | R | - | R | +| Permissions | W | - | W | W | R | +| Projects | W | - | W | R | R | +| Repositories | W | - | W | - | R | +| Runs | W | - | W | - | R | +| Semantic Layer config | W | - | W | v | R | +`} + ### Project role permissions The project roles enable you to work within the projects in various capacities. They primarily provide access to project-level permissions such as repos and the IDE or dbt Cloud CLI, but may also provide some account-level permissions. +Key: + +* (W)rite — Create new or modify existing. Includes `send`, `create`, `delete`, `allocate`, `modify`, and `develop`. +* (R)ead — Can view but can not create or change any fields. + #### Account permissions for project roles -| Account-level permission | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job runner | Job viewer | Metadata

(Discovery API only) | Semantic Layer | Stakeholder | Team admin | Webhook | + + +{` +| Account-level permission | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job runner | Job viewer | Metadata (Discovery API only) | Semantic Layer | Stakeholder | Team admin | Webhook | |--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:-------:| -| Account settings | R | | R | | R | | | | | | | R | | -| Auth provider | | | | | | | | | | | | | | -| Billing | | | | | | | | | | | | | | -| Connections | R | R | R | R | R | R | | | | | R | R | | -| Groups | R | | R | R | R | | | | | | R | R | | -| Invitations | W | R | R | R | R | R | | R | | | R | R | | -| Licenses | W | R | R | R | R | R | | R | | | | R | | -| Members | W | | R | R | R | | | | | | R | R | | -| Project (create) | | | | | | | | | | | | | | -| Public models | R | R | R | R | R | R | | R | R | R | R | R | R | -| Service tokens | | | | | | | | | | | | | | -| Webhooks | W | | | W | | | | | | | | | W | +| Account settings | R | - | R | - | R | - | - | - | - | - | - | R | - | +| Auth provider | - | - | - | - | - | - | - | - | - | - | - | - | - | +| Billing | - | - | - | - | - | - | - | - | - | - | - | - | - | +| Connections | R | R | R | R | R | R | - | - | - | - | R | R | - | +| Groups | R | - | R | R | R | - | - | - | - | - | R | R | - | +| Invitations | W | R | R | R | R | R | - | R | - | - | R | R | - | +| Licenses | W | R | R | R | R | R | - | R | - | - | - | R | - | +| Members | W | - | R | R | R | - | - | - | - | - | R | R | - | +| Project (create) | - | - | - | - | - | - | - | - | - | - | - | - | - | +| Public models | R | R | R | R | R | R | - | R | R | R | R | R | R | +| Service tokens | - | - | - | - | - | - | - | - | - | - | - | - | - | +| Webhooks | W | - | - | W | - | - | - | - | - | - | - | - | W | +`} + + #### Project permissions for project roles - -|Project-level permission | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job runner | Job viewer | Metadata

(Discovery API only) | Semantic Layer | Stakeholder | Team admin | Webhook | -|--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:-------:| -| Environment credentials (deployment) | W | W | W | W | R | W | | | | | R | R | | -| Custom env. variables | W | W | W | W | W | W | | R | | | R | W | | -| Data platform configurations| W | W | W | W | R | W | | | | | R | R | | -| Develop
(IDE or dbt Cloud CLI) | W | W | | W | | | | | | | | | | -| Environments | W | R | R | R | R | W | | R | | | R | R | | -| Jobs | W | R | R | R | R | W | R | R | | | R | R | | -| Metadata GraphQL API access | R | R | R | R | R | R | | R | R | | R | R | | -| Permissions (Groups & Licenses) | W | | R | R | R | | | | | | | R | | | | | R | | | -| Projects | W | W | W | W | W | R | | R | | | R | W | | -| Repositories | W | | R | R | W | | | | | | R | R | | -| Runs | W | R | R | R | R | W | W | R | | | R | R | | -| Semantic Layer config | W | R | W | R | R | R | | | | W | R | R | | + + + +{` +|Project-level permission | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job runner | Job viewer | Metadata (Discovery API only) | Semantic Layer | Stakeholder | Team admin | Webhook | +|--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:-----------:|:---------------------------------------:|:--------------:|:-----------:|:----------:|:-------:| +| Environment credentials | W | W | W | W | R | W | - | - | - | - | R | R | - | +| Custom env. variables | W | W | W | W | W | W | - | R | - | - | R | W | - | +| Data platform configs | W | W | W | W | R | W | - | - | - | - | R | R | - | +| Develop (IDE or CLI) | W | W | - | W | - | - | - | - | - | - | - | - | - | +| Environments | W | R | R | R | R | W | - | R | - | - | R | R | - | +| Jobs | W | R* | R* | R* | R* | W | R | R | - | - | R | R* | - | +| Metadata GraphQL API access| R | R | R | R | R | R | - | R | R | - | R | R | - | +| Permissions | W | - | R | R | R | - | - | - | - | - | - | R | - | +| Projects | W | W | W | W | W | R | - | R | - | - | R | W | - | +| Repositories | W | - | R | R | W | - | - | - | - | - | R | R | - | +| Runs | W | R* | R* | R* | R* | W | W | R | - | - | R | R* | - | +| Semantic Layer config | W | R | W | R | R | R | - | - | - | W | R | R | - | + +`} + + + +\* These permissions are `R`ead-only by default, but may be changed to `W`rite with [environment permissions](/docs/cloud/manage-access/environment-permissions#environments-and-roles). diff --git a/website/snippets/_hard-deletes.md b/website/snippets/_hard-deletes.md new file mode 100644 index 00000000000..59c2e3af99e --- /dev/null +++ b/website/snippets/_hard-deletes.md @@ -0,0 +1,13 @@ + + +**Use `invalidate_hard_deletes` (v1.8 and earlier) if:** +- Gaps in the snapshot history (missing records for deleted rows) are acceptable. +- You want to invalidate deleted rows by setting their `dbt_valid_to` timestamp to the current time (implicit delete). +- You are working with smaller datasets where tracking deletions as a separate state is unnecessary. + +**Use `hard_deletes: new_record` (v1.9 and higher) if:** +- You want to maintain continuous snapshot history without gaps. +- You want to explicitly track deletions by adding new rows with a `dbt_is_deleted` column (explicit delete). +- You are working with larger datasets where explicitly tracking deleted records improves data lineage clarity. + + diff --git a/website/snippets/_legacy-snapshot-config.md b/website/snippets/_legacy-snapshot-config.md new file mode 100644 index 00000000000..a38995308e9 --- /dev/null +++ b/website/snippets/_legacy-snapshot-config.md @@ -0,0 +1,4 @@ + +:::info +Starting from [the dbt Cloud "Latest" release track](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9, defining snapshots in a `.sql` file using a config block is a legacy method. You can define snapshots in YAML format using the latest [snapshot-specific configurations](/docs/build/snapshots#configuring-snapshots). For new snapshots, we recommend using these latest configs. If applying them to existing snapshots, you'll need to [migrate](#snapshot-configuration-migration) over. +::: diff --git a/website/snippets/_log-relational-cache.md b/website/snippets/_log-relational-cache.md new file mode 100644 index 00000000000..4249030f94e --- /dev/null +++ b/website/snippets/_log-relational-cache.md @@ -0,0 +1,5 @@ +

The `LOG_CACHE_EVENTS` config allows detailed logging for {props.event}, which are disabled by default.

+ +```text +dbt --log-cache-events compile +``` diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md index 39cd2b22b9a..eccd6db4c09 100644 --- a/website/snippets/_new-sl-setup.md +++ b/website/snippets/_new-sl-setup.md @@ -35,17 +35,22 @@ This credential controls the physical access to underlying data accessed by the *If you're on a Team plan and need to add more credentials, consider upgrading to our [Enterprise plan](https://www.getdbt.com/contact). Enterprise users can refer to [Add more credentials](#4-add-more-credentials) for detailed steps on adding multiple credentials.* -1. After selecting the deployment environment, you should see the **Credentials & service tokens** page. -2. Click the **Add Semantic Layer credential** button. -3. In the **1. Add credentials** section, enter the credentials specific to your data platform that you want the Semantic Layer to use. +#### 1. Select deployment environment + - After selecting the deployment environment, you should see the **Credentials & service tokens** page. + - Click the **Add Semantic Layer credential** button. + +#### 2. Configure credential + - In the **1. Add credentials** section, enter the credentials specific to your data platform that you want the Semantic Layer to use. - Use credentials with minimal privileges. The Semantic Layer requires read access to the schema(s) containing the dbt models used in your semantic models for downstream applications - -4. After adding credentials, scroll to **2. Map new service token**. -5. Name the token and ensure the permission set includes 'Semantic Layer Only' and 'Metadata Only'. -6. Click **Save**. Once the token is generated, you won't be able to view this token again so make sure to record it somewhere safe. +#### 3. Create or link service tokens + - If you have permission to create service tokens, you’ll see the [**Map new service token** option](/docs/use-dbt-semantic-layer/setup-sl#map-service-tokens-to-credentials) after adding the credential. Name the token, set permissions to 'Semantic Layer Only' and 'Metadata Only', and click **Save**. + - Once the token is generated, you won't be able to view this token again, so make sure to record it somewhere safe. + - If you don’t have access to create service tokens, you’ll see a message prompting you to contact your admin to create one for you. Admins can create and link tokens as needed. + :::info - Team plans can create multiple service tokens that link to a single underlying credential, but each project can only have one credential. @@ -67,26 +72,28 @@ dbt Cloud Enterprise plans can optionally add multiple credentials and map them We recommend configuring credentials and service tokens to reflect your teams and their roles. For example, create tokens or credentials that align with your team's needs, such as providing access to finance-related schemas to the Finance team. -Note that: + + - Admins can link multiple service tokens to a single credential within a project, but each service token can only be linked to one credential per project. - When you send a request through the APIs, the service token of the linked credential will follow access policies of the underlying view and tables used to build your semantic layer requests. - - -To add multiple credentials and map them to service tokens: - -1. After configuring your environment, on the **Credentials & service tokens** page, click the **Add Semantic Layer credential** button to create multiple credentials and map them to a service token. -2. In the **1. Add credentials** section, fill in the data platform's credential fields. We recommend using “read-only” credentials. - - -3. In the **2. Map new service token** section, map a service token to the credential you configured in the previous step. dbt Cloud automatically selects the service token permission set you need (Semantic Layer Only and Metadata Only). - -4. To add another service token during configuration, click **Add Service Token**. -5. You can link more service tokens to the same credential later on in the **Semantic Layer Configuration Details** page. To add another service token to an existing Semantic Layer configuration, click **Add service token** under the **Linked service tokens** section. -6. Click **Save** to link the service token to the credential. Remember to copy and save the service token securely, as it won't be viewable again after generation. + + +#### 1. Add more credentials +- After configuring your environment, on the **Credentials & service tokens** page, click the **Add Semantic Layer credential** button to create multiple credentials and map them to a service token.
+- In the **1. Add credentials** section, fill in the data platform's credential fields. We recommend using “read-only” credentials. + + +#### 2. Map service tokens to credentials +- In the **2. Map new service token** section, [map a service token to the credential](/docs/use-dbt-semantic-layer/setup-sl#map-service-tokens-to-credentials) you configured in the previous step. dbt Cloud automatically selects the service token permission set you need (Semantic Layer Only and Metadata Only). +- To add another service token during configuration, click **Add Service Token**. +- You can link more service tokens to the same credential later on in the **Semantic Layer Configuration Details** page. To add another service token to an existing Semantic Layer configuration, click **Add service token** under the **Linked service tokens** section. +- Click **Save** to link the service token to the credential. Remember to copy and save the service token securely, as it won't be viewable again after generation. -7. To delete a credential, go back to the **Credentials & service tokens** page. -8. Under **Linked Service Tokens**, click **Edit** and, select **Delete Credential** to remove a credential. +#### 3. Delete credentials +- To delete a credential, go back to the **Credentials & service tokens** page. +- Under **Linked Service Tokens**, click **Edit** and, select **Delete Credential** to remove a credential. When you delete a credential, any service tokens mapped to that credential in the project will no longer work and will break for any end users. @@ -107,6 +114,15 @@ To re-enable the dbt Semantic Layer setup in the future, you will need to recrea The following are the additional flexible configurations for Semantic Layer credentials. +### Map service tokens to credentials +- After configuring your environment, you can map additional service tokens to the same credential if you have the required [permissions](/docs/cloud/manage-access/about-user-access#permission-sets). +- Go to the **Credentials & service tokens** page and click the **+Add Service Token** button in the **Linked Service Tokens** section. +- Type the service token name and select the permission set you need (Semantic Layer Only and Metadata Only). +- Click **Save** to link the service token to the credential. +- Remember to copy and save the service token securely, as it won't be viewable again after generation. + + + ### Unlink service tokens - Unlink a service token from the credential by clicking **Unlink** under the **Linked service tokens** section. If you try to query the Semantic Layer with an unlinked credential, you'll experience an error in your BI tool because no valid token is mapped. @@ -115,7 +131,7 @@ To re-enable the dbt Semantic Layer setup in the future, you will need to recrea - View your Semantic Layer credential directly by navigating to the **API tokens** and then **Service tokens** page. - Select the service token to view the credential it's linked to. This is useful if you want to know which service tokens are mapped to credentials in your project. -**Create a new service token** +#### Create a new service token - From the **Service tokens** page, create a new service token and map it to the credential(s) (assuming the semantic layer permission exists). This is useful if you want to create a new service token and directly map it to a credential in your project. - Make sure to select the correct permission set for the service token (Semantic Layer Only and Metadata Only). diff --git a/website/snippets/_relative-path.md b/website/snippets/_relative-path.md new file mode 100644 index 00000000000..791f8e83f7e --- /dev/null +++ b/website/snippets/_relative-path.md @@ -0,0 +1 @@ +Paths specified in {props.path} must be relative to the location of your `dbt_project.yml` file. Avoid using absolute paths like {props.absolute}, as it will lead to unexpected behavior and outcomes. diff --git a/website/snippets/_release-stages-from-versionless.md b/website/snippets/_release-stages-from-versionless.md new file mode 100644 index 00000000000..f6fbf9153b0 --- /dev/null +++ b/website/snippets/_release-stages-from-versionless.md @@ -0,0 +1,5 @@ +:::note Versionless is now the "latest" release track + +This blog post was updated on December 04, 2024 to rename "versionless" to the "latest" release track allowing for the introduction of less-frequent release tracks. Learn more about [Release Tracks](/docs/dbt-versions/cloud-release-tracks) and how to use them. + +::: diff --git a/website/snippets/_sl-measures-parameters.md b/website/snippets/_sl-measures-parameters.md index 728d63c6b4f..8d6b84a71dd 100644 --- a/website/snippets/_sl-measures-parameters.md +++ b/website/snippets/_sl-measures-parameters.md @@ -1,11 +1,11 @@ -| Parameter | Description | | -| --- | --- | --- | -| [`name`](/docs/build/measures#name) | Provide a name for the measure, which must be unique and can't be repeated across all semantic models in your dbt project. | Required | -| [`description`](/docs/build/measures#description) | Describes the calculated measure. | Optional | -| [`agg`](/docs/build/measures#aggregation) | dbt supports the following aggregations: `sum`, `max`, `min`, `average`, `median`, `count_distinct`, `percentile`, and `sum_boolean`. | Required | -| [`expr`](/docs/build/measures#expr) | Either reference an existing column in the table or use a SQL expression to create or derive a new one. | Optional | -| [`non_additive_dimension`](/docs/build/measures#non-additive-dimensions) | Non-additive dimensions can be specified for measures that cannot be aggregated over certain dimensions, such as bank account balances, to avoid producing incorrect results. | Optional | -| `agg_params` | Specific aggregation properties, such as a percentile. | Optional | -| `agg_time_dimension` | The time field. Defaults to the default agg time dimension for the semantic model. | Optional | 1.6 and higher | -| `label` | String that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as orders_total or "orders_total"). Available in dbt version 1.7 or higher. | Optional -| `create_metric` | Create a `simple` metric from a measure by setting `create_metric: True`. The `label` and `description` attributes will be automatically propagated to the created metric. Available in dbt version 1.7 or higher. | Optional | +| Parameter | Description | Required | Type | +| --- | --- | --- | --- | +| [`name`](/docs/build/measures#name) | Provide a name for the measure, which must be unique and can't be repeated across all semantic models in your dbt project. | Required | String | +| [`description`](/docs/build/measures#description) | Describes the calculated measure. | Optional | String | +| [`agg`](/docs/build/measures#aggregation) | dbt supports the following aggregations: `sum`, `max`, `min`, `average`, `median`, `count_distinct`, `percentile`, and `sum_boolean`. | Required | String | +| [`expr`](/docs/build/measures#expr) | Either reference an existing column in the table or use a SQL expression to create or derive a new one. | Optional | String | +| [`non_additive_dimension`](/docs/build/measures#non-additive-dimensions) | Non-additive dimensions can be specified for measures that cannot be aggregated over certain dimensions, such as bank account balances, to avoid producing incorrect results. | Optional | String | +| `agg_params` | Specific aggregation properties, such as a percentile. | Optional | Dict | +| `agg_time_dimension` | The time field. Defaults to the default agg time dimension for the semantic model. | Optional | String | +| `label` | String that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as `orders_total` or `"orders_total"`). Available in dbt version 1.7 or higher. | Optional | String | +| `create_metric` | Create a `simple` metric from a measure by setting `create_metric: True`. The `label` and `description` attributes will be automatically propagated to the created metric. Available in dbt version 1.7 or higher. | Optional | Boolean | diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md index aaefcc77747..7d08323239b 100644 --- a/website/snippets/_sl-partner-links.md +++ b/website/snippets/_sl-partner-links.md @@ -22,6 +22,20 @@ The following tools integrate with the dbt Semantic Layer: body="Connect to Microsoft Excel to query metrics and collaborate with your team. Available for Excel Desktop or Excel Online." icon="excel"/> +
+ + + + +
+
+ -| Region | dbt Cloud Access URL | Auth0 SSO URI <YOUR_AUTH0_URI> | Auth0 Entity ID <YOUR_AUTH0_ENTITYID>* | -|--------|-----------------------|-------------------------------|----------------------------------------| -| US multi-tenant | cloud.getdbt.com | auth.cloud.getdbt.com | us-production-mt | -| US cell 1 | \{account prefix\}.us1.dbt.com | auth.cloud.getdbt.com | us-production-mt | -| EMEA | emea.dbt.com | auth.emea.dbt.com | emea-production-mt | -| APAC | au.dbt.com | auth.au.dbt.com | au-production-mt | -*Only applicable to SAML and Okta configurations. diff --git a/website/snippets/cloud-feature-parity.md b/website/snippets/cloud-feature-parity.md index 3f7f447050c..9348f4d409b 100644 --- a/website/snippets/cloud-feature-parity.md +++ b/website/snippets/cloud-feature-parity.md @@ -6,6 +6,7 @@ The following table outlines which dbt Cloud features are supported on the diffe | Continuous integration jobs | ✅ | ✅ | ✅ | ✅ | | dbt Cloud CLI | ✅ | ✅ | ✅ | ✅ | | dbt Cloud IDE | ✅ | ✅ | ✅ | ✅ | +| dbt Copilot | ✅ | ✅ | ✅ | ✅ | | dbt Explorer | ✅ | ✅ | ✅ | ✅ | | dbt Mesh | ✅ | ✅ | ✅ | ✅ | | dbt Semantic Layer | ✅ | ✅ (Upon request) | ✅ | ❌ | diff --git a/website/snippets/core-versions-table.md b/website/snippets/core-versions-table.md index 743b59c6bb7..0d82ab35573 100644 --- a/website/snippets/core-versions-table.md +++ b/website/snippets/core-versions-table.md @@ -2,7 +2,8 @@ | dbt Core | Initial release | Support level and end date | |:-------------------------------------------------------------:|:---------------:|:-------------------------------------:| -| [**v1.8**](/docs/dbt-versions/core-upgrade/upgrading-to-v1.8) | May 9 2024 | Active Support — May 8, 2025 | +| [**v1.9**](/docs/dbt-versions/core-upgrade/upgrading-to-v1.9) | Dec 9, 2024 | Active Support — Dec 8, 2025| +| [**v1.8**](/docs/dbt-versions/core-upgrade/upgrading-to-v1.8) | May 9, 2024 | Active Support — May 8, 2025| | [**v1.7**](/docs/dbt-versions/core-upgrade/upgrading-to-v1.7) | Nov 2, 2023 |
**dbt Core and dbt Cloud Developer & Team customers:** End of Life
**dbt Cloud Enterprise customers:** Critical Support until further notice 1
| | [**v1.6**](/docs/dbt-versions/core-upgrade/upgrading-to-v1.6) | Jul 31, 2023 | End of Life ⚠️ | | [**v1.5**](/docs/dbt-versions/core-upgrade/upgrading-to-v1.5) | Apr 27, 2023 | End of Life ⚠️ | @@ -13,8 +14,8 @@ | [**v1.0**](/docs/dbt-versions/core-upgrade/Older%20versions/upgrading-to-v1.0) | Dec 3, 2021 | End of Life ⚠️ | | **v0.X** ⛔️ | (Various dates) | Deprecated ⛔️ | Deprecated ⛔️ | -All functionality in dbt Core since the v1.7 release is available in dbt Cloud, early and continuously, by selecting ["Versionless"](https://docs.getdbt.com/docs/dbt-versions/versionless-cloud). +All functionality in dbt Core since the v1.7 release is available in [dbt Cloud release tracks](/docs/dbt-versions/cloud-release-tracks), which provide automated upgrades at a cadence appropriate for your team. -1 "Versionless" is now required for the Developer and Teams plans on dbt Cloud. Accounts using older dbt versions will be migrated to "Versionless." +1 Release tracks are required for the Developer and Teams plans on dbt Cloud. Accounts using older dbt versions will be migrated to the "Latest" release track. -For customers of dbt Cloud Enterprise, dbt v1.7 will continue to be available as an option while dbt Labs rolls out a mechanism for "extended" upgrades. In the meantime, dbt Labs strongly recommends migrating any environments that are still running on older unsupported versions to "Versionless" dbt or dbt v1.7. +For customers of dbt Cloud Enterprise, dbt v1.7 will continue to be available as an option until dbt Labs announces that "Compatible" and "Extended" release tracks are Generally Available, planned for March 2025. (They are currently available to all eligible accounts in Preview.) In the meantime, dbt Labs strongly recommends migrating any environments that are still running on older unsupported versions to either release tracks or dbt v1.7. diff --git a/website/snippets/hooks-to-grants.md b/website/snippets/hooks-to-grants.md deleted file mode 100644 index d7586ec53ca..00000000000 --- a/website/snippets/hooks-to-grants.md +++ /dev/null @@ -1,3 +0,0 @@ - -In older versions of dbt, the most common use of `post-hook` was to execute `grant` statements, to apply database permissions to models right after creating them. We recommend using the [`grants` resource config](/reference/resource-configs/grants) instead, in order to automatically apply grants when your dbt model runs. - diff --git a/website/src/components/expandable/styles.module.css b/website/src/components/expandable/styles.module.css index fc6f258286b..4d3957228b9 100644 --- a/website/src/components/expandable/styles.module.css +++ b/website/src/components/expandable/styles.module.css @@ -145,4 +145,5 @@ .headerText { display: flex; align-items: center; -} \ No newline at end of file +} + diff --git a/website/src/components/sortableTable/index.js b/website/src/components/sortableTable/index.js new file mode 100644 index 00000000000..93d54252c94 --- /dev/null +++ b/website/src/components/sortableTable/index.js @@ -0,0 +1,114 @@ +import React, { useState, useMemo } from 'react'; +import Markdown from 'markdown-to-jsx'; + +const stripMarkdown = (text) => { + let strippedText = text.replace(/\[([^\]]+)\]\([^)]+\)/g, '$1'); + strippedText = strippedText.replace(/[_*`~]/g, ''); + return strippedText; +}; + +const parseMarkdownTable = (markdown) => { + const rows = markdown.trim().split('\n'); + const headers = rows[0].split('|').map((header) => header.trim()).filter(Boolean); + + const alignmentsRow = rows[1].split('|').map((align) => align.trim()).filter(Boolean); + const columnAlignments = alignmentsRow.map((alignment) => { + if (alignment.startsWith(':') && alignment.endsWith(':')) { + return 'center'; + } else if (alignment.startsWith(':')) { + return 'left'; + } else if (alignment.endsWith(':')) { + return 'right'; + } else { + return 'left'; + } + }); + + const data = rows.slice(2).map(row => row.split('|').map(cell => cell.trim()).filter(Boolean)); + + return { headers, data, columnAlignments }; +}; + +const SortableTable = ({ children }) => { + const { headers, data: initialData, columnAlignments } = useMemo( + () => parseMarkdownTable(children), + [children] + ); + + const [data, setData] = useState(initialData); + const [sortConfig, setSortConfig] = useState({ key: '', direction: 'asc' }); + + const sortTable = (keyIndex) => { + const newDirection = (sortConfig.key === keyIndex && sortConfig.direction === 'asc') ? 'desc' : 'asc'; + setSortConfig({ key: keyIndex, direction: newDirection }); + + const sortedData = [...data].sort((a, b) => { + const aVal = stripMarkdown(a[keyIndex]); + const bVal = stripMarkdown(b[keyIndex]); + if (aVal < bVal) return newDirection === 'asc' ? -1 : 1; + if (aVal > bVal) return newDirection === 'asc' ? 1 : -1; + return 0; + }); + + setData(sortedData); + }; + + return ( + + + + {headers.map((header, index) => ( + + ))} + + + + {data.map((row, rowIndex) => ( + + {row.map((cell, cellIndex) => ( + + ))} + + ))} + +
sortTable(index)} + style={{ + cursor: 'pointer', + position: 'relative', + textAlign: columnAlignments[index], + padding: '10px' + }} + > +
+ {header} + + ↑ + + + ↓ + +
+
+ {cell || '\u00A0'} +
+ ); +}; + +export default SortableTable; diff --git a/website/src/css/custom.css b/website/src/css/custom.css index e240a5dfabf..b8979ffc943 100644 --- a/website/src/css/custom.css +++ b/website/src/css/custom.css @@ -2112,3 +2112,35 @@ h2.anchor.clicked a.hash-link:before { flex-direction: column; } } + +.markdown table th, +.markdown table td { + padding: 8px; + border: 1px solid var(--table-border-color); + word-wrap: break-word; + white-space: normal; + text-align: left; +} + +table th { + background-color: #ED7254; /* Table header background color */ +} + +:root { + --table-border-color: #000000; /* Light mode table border color */ +} + +/* Dark mode border */ +[data-theme="dark"] { + --table-border-color: #ddd; /* Dark mode table border color */ +} +table th { + color: #ffffff; /* White text on lighter background */ + font-weight: bold; +} + +/* Dark mode table header text */ +[data-theme='dark'] table th { + color: #000000; /* Black text on darker background */ + font-weight: bold; +} \ No newline at end of file diff --git a/website/src/theme/MDXComponents/index.js b/website/src/theme/MDXComponents/index.js index d136222a0ce..422d6c99fab 100644 --- a/website/src/theme/MDXComponents/index.js +++ b/website/src/theme/MDXComponents/index.js @@ -13,6 +13,7 @@ import Mermaid from '@theme/Mermaid'; /* dbt Customizations: * Imports the following components below for export */ +import SortableTable from '@site/src/components/sortableTable'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem' import Changelog from '@site/src/components/changelog'; @@ -95,5 +96,6 @@ const MDXComponents = { DetailsToggle: DetailsToggle, Expandable: Expandable, ConfettiTrigger: ConfettiTrigger, + SortableTable: SortableTable, }; export default MDXComponents; diff --git a/website/static/img/adapter-guide/0-full-release-notes.png b/website/static/img/adapter-guide/0-full-release-notes.png index 6cb9f0ae8ed..284343ff955 100644 Binary files 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