Merge branch 'current' into patch-1

website/docs/best-practices/clone-incremental-models.md

@@ -0,0 +1,79 @@
+---
+title: "Clone incremental models as the first step of your CI job"
+id: "clone-incremental-models"
+description: Learn how to define clone incremental models as the first step of your CI job.
+displayText: Clone incremental models as the first step of your CI job
+hoverSnippet: Learn how to clone incremental models for CI jobs.
+---
+
+Before you begin, you must be aware of a few conditions:
+- `dbt clone` is only available with dbt version 1.6 and newer. Refer to our [upgrade guide](/docs/dbt-versions/upgrade-core-in-cloud) for help enabling newer versions in dbt Cloud
+- This strategy only works for warehouse that support zero copy cloning (otherwise `dbt clone` will just create pointer views).
+- Some teams may want to test that their incremental models run in both incremental mode and full-refresh mode.
+
+Imagine you've created a [Slim CI job](/docs/deploy/continuous-integration) in dbt Cloud and it is configured to: 
+
+- Defer to your production environment.
+- Run the command `dbt build --select state:modified+` to run and test all of the models you've modified and their downstream dependencies.
+- Trigger whenever a developer on your team opens a PR against the main branch.
+
+<Lightbox src="/img/best-practices/slim-ci-job.png" width="70%" title="Example of a slim CI job with the above configurations" />
+
+Now imagine your dbt project looks something like this in the DAG:
+
+<Lightbox src="/img/best-practices/dag-example.png" width="70%" title="Sample project DAG" />
+
+When you open a pull request (PR) that modifies `dim_wizards`, your CI job will kickoff and build _only the modified models and their downstream dependencies_ (in this case, `dim_wizards` and `fct_orders`) into a temporary schema that's unique to your PR. 
+
+This build mimics the behavior of what will happen once the PR is merged into the main branch. It ensures you're not introducing breaking changes, without needing to build your entire dbt project. 
+
+## What happens when one of the modified models (or one of their downstream dependencies) is an incremental model?
+
+Because your CI job is building modified models into a PR-specific schema, on the first execution of `dbt build --select state:modified+`, the modified incremental model will be built in its entirety _because it does not yet exist in the PR-specific schema_ and [is_incremental will be false](/docs/build/incremental-models#understanding-the-is_incremental-macro). You're running in `full-refresh` mode.
+
+This can be suboptimal because:
+- Typically incremental models are your largest datasets, so they take a long time to build in their entirety which can slow down development time and incur high warehouse costs.
+- There are situations where a `full-refresh` of the incremental model passes successfully in your CI job but an _incremental_ build of that same table in prod would fail when the PR is merged into main (think schema drift where [on_schema_change](/docs/build/incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`)
+
+You can alleviate these problems by zero copy cloning the relevant, pre-exisitng incremental models into your PR-specific schema as the first step of the CI job using the `dbt clone` command. This way, the incremental models already exist in the PR-specific schema when you first execute the command `dbt build --select state:modified+` so the `is_incremental` flag will be `true`. 
+
+You'll have two commands for your dbt Cloud CI check to execute:
+1. Clone all of the pre-existing incremental models that have been modified or are downstream of another model that has been modified: `dbt clone --select state:modified+,config.materialized:incremental,state:old`
+2. Build all of the models that have been modified and their downstream dependencies: `dbt build --select state:modified+`
+
+Because of your first clone step, the incremental models selected in your `dbt build` on the second step will run in incremental mode.
+
+<Lightbox src="/img/best-practices/clone-command.png" width="70%" title="Clone command in the CI config" />
+
+Your CI jobs will run faster, and you're more accurately mimicking the behavior of what will happen once the PR has been merged into main. 
+
+### Expansion on "think schema drift" where [on_schema_change](/docs/build/incremental-models#what-if-the-columns-of-my-incremental-model-change) config is set to `fail`" from above
+
+Imagine you have an incremental model `my_incremental_model` with the following config:
+
+```sql
+
+{{
+    config(
+        materialized='incremental',
+        unique_key='unique_id',
+        on_schema_change='fail'
+    )
+}}
+
+```
+
+Now, let’s say you open up a PR that adds a new column to `my_incremental_model`. In this case:
+- An incremental build will fail.
+- A `full-refresh` will succeed.
+
+If you have a daily production job that just executes `dbt build` without a `--full-refresh` flag, once the PR is merged into main and the job kicks off, you will get a failure. So the question is - what do you want to happen in CI?
+- Do you want to also get a failure in CI, so that you know that once this PR is merged into main you need to immediately execute a `dbt build --full-refresh --select my_incremental_model` in production in order to avoid a failure in prod? This will block your CI check from passing.
+- Do you want your CI check to succeed, because once you do run a `full-refresh` for this model in prod you will be in a successful state? This may lead unpleasant surprises if your production job is suddenly failing when you merge this PR into main if you don’t remember you need to execute a `dbt build --full-refresh --select my_incremental_model` in production.
+
+There’s probably no perfect solution here; it’s all just tradeoffs! Our preference would be to have the failing CI job and have to manually override the blocking branch protection rule so that there are no surprises and we can proactively run the appropriate command in production once the PR is merged. 
+
+### Expansion on "why `state:old`"
+
+For brand new incremental models, you want them to run in `full-refresh` mode in CI, because they will run in `full-refresh` mode in production when the PR is merged into `main`. They also don't exist yet in the production environment... they're brand new!
+If you don't specify this, you won't get an error just a “No relation found in state manifest for…”. So, it technically works without specifying `state:old` but adding `state:old` is more explicit and means it won't even try to clone the brand new incremental models.

website/docs/community/resources/getting-help.md

@@ -60,4 +60,4 @@ If you want to receive dbt training, check out our [dbt Learn](https://learn.get
 - Billing
 - Bug reports related to the web interface
 
-As a rule of thumb, if you are using dbt Cloud, but your problem is related to code within your dbt project, then please follow the above process rather than reaching out to support.
+As a rule of thumb, if you are using dbt Cloud, but your problem is related to code within your dbt project, then please follow the above process rather than reaching out to support. Refer to [dbt Cloud support](/docs/dbt-support) for more information.

website/docs/docs/build/cumulative-metrics.md

@@ -38,10 +38,7 @@ metrics:
 
 ## Limitations
 Cumulative metrics are currently under active development and have the following limitations:
-
-1. You can only use the [`metric_time` dimension](/docs/build/dimensions#time) to check cumulative metrics. If you don't use `metric_time` in the query, the cumulative metric will return incorrect results because it won't perform the time spine join. This means you cannot reference time dimensions other than the `metric_time` in the query.
-2. If you use `metric_time` in your query filter but don't include "start_time" and "end_time," cumulative metrics will left-censor the input data. For example, if you query a cumulative metric with a 7-day window with the filter `{{ TimeDimension('metric_time') }} BETWEEN '2023-08-15' AND '2023-08-30' `, the values for `2023-08-15` to `2023-08-20` return missing or incomplete data. This is because we apply the `metric_time` filter to the aggregation input. To avoid this, you must use `start_time` and `end_time` in the query filter.
-
+- You are required to use [`metric_time` dimension](/docs/build/dimensions#time) when querying cumulative metrics. If you don't use `metric_time` in the query, the cumulative metric will return incorrect results because it won't perform the time spine join. This means you cannot reference time dimensions other than the `metric_time` in the query.
 
 ## Cumulative metrics example
 

website/docs/docs/build/dimensions.md

@@ -15,7 +15,8 @@ In a data platform, dimensions is part of a larger structure called a semantic m
 
 Groups are defined within semantic models, alongside entities and measures, and correspond to non-aggregatable columns in your dbt model that provides categorical or time-based context. In SQL, dimensions  is typically included in the GROUP BY clause.-->
 
-All dimensions require a `name`, `type` and in some cases, an `expr` parameter. 
+All dimensions require a `name`, `type` and in some cases, an `expr` parameter. The `name` for your dimension must be unique to the semantic model and can not be the same as an existing `entity` or `measure` within that same model.
+
 
 | Parameter | Description | Type |
 | --------- | ----------- | ---- |

website/docs/docs/build/entities.md

@@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer]
 
 Entities are real-world concepts in a business such as customers, transactions, and ad campaigns. We often focus our analyses around specific entities, such as customer churn or annual recurring revenue modeling. We represent entities in our semantic models using id columns that serve as join keys to other semantic models in your semantic graph.
 
-Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter.
+Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter. The `name` for your entity must be unique to the semantic model and can not be the same as an existing `measure` or `dimension` within that same model.
 
 Entities can be specified with a single column or multiple columns. Entities (join keys) in a semantic model are identified by their name. Each entity name must be unique within a semantic model, but it doesn't have to be unique across different semantic models. 
 

website/docs/docs/build/materializations.md

@@ -14,6 +14,8 @@ pagination_next: "docs/build/incremental-models"
 - ephemeral
 - materialized view
 
+You can also configure [custom materializations](/guides/create-new-materializations?step=1) in dbt. Custom materializations are a powerful way to extend dbt's functionality to meet your specific needs. 
+
 
 ## Configuring materializations
 By default, dbt models are materialized as "views". Models can be configured with a different materialization by supplying the `materialized` configuration parameter as shown below.

website/docs/docs/build/measures.md

@@ -34,7 +34,8 @@ measures:
 
 When you create a measure, you can either give it a custom name or use the `name` of the data platform column directly. If the `name` of the measure is different from the column name, you need to add an `expr` to specify the column name. The `name` of the measure is used when creating a metric. 
 
-Measure names must be **unique** across all semantic models in a project.
+Measure names must be unique across all semantic models in a project and can not be the same as an existing `entity` or `dimension` within that same model.
+
 
 ### Description
 

website/docs/docs/build/sl-getting-started.md

@@ -74,21 +74,9 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 If you're encountering some issues when defining your metrics or setting up the dbt Semantic Layer, check out a list of answers to some of the questions or problems you may be experiencing.
 
-<details>
-  <summary>How do I migrate from the legacy Semantic Layer to the new one?</summary>
-  <div>
-    <div>If you're using the legacy Semantic Layer, we highly recommend you <a href="https://docs.getdbt.com/docs/dbt-versions/upgrade-core-in-cloud">upgrade your dbt version </a> to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated <a href="https://docs.getdbt.com/guides/sl-migration"> migration guide</a> for more info.</div>
-  </div>
-</details>
-<details>
-<summary>How are you storing my data?</summary>
-User data passes through the Semantic Layer on its way back from the warehouse. dbt Labs ensures security by authenticating through the customer's data warehouse. Currently, we don't cache data for the long term, but it might temporarily stay in the system for up to 10 minutes, usually less. In the future, we'll introduce a caching feature that allows us to cache data on our infrastructure for up to 24 hours.
-</details>
-
-<details>
-<summary>Is the dbt Semantic Layer open source?</summary>
-The dbt Semantic Layer is proprietary; however, some components of the dbt Semantic Layer are open source, such as dbt-core and MetricFlow. <br /><br />dbt Cloud Developer or dbt Core users can define metrics in their project, including a local dbt Core project, using the dbt Cloud IDE, dbt Cloud CLI, or dbt Core CLI. However, to experience the universal dbt Semantic Layer and access those metrics using the API or downstream tools, users must be on a dbt Cloud <a href="https://www.getdbt.com/pricing/">Team or Enterprise</a> plan. <br /><br />Refer to <a href="https://docs.getdbt.com/docs/cloud/billing">Billing</a> for more information.
-</details>
+import SlFaqs from '/snippets/_sl-faqs.md';
+
+<SlFaqs/>
 
 
 ## Next steps

website/docs/docs/cloud/billing.md

@@ -126,6 +126,8 @@ All included successful models built numbers above reflect our most current pric
 
 As an Enterprise customer, you pay annually via invoice, monthly in arrears for additional usage (if applicable), and may benefit from negotiated usage rates. Please refer to your order form or contract for your specific pricing details, or [contact the account team](https://www.getdbt.com/contact-demo) with any questions. 
 
+Enterprise plan billing information is not available in the dbt Cloud UI.  Changes are handled through your dbt Labs Solutions Architect or account team manager.
+
 ### Legacy plans
 
 Customers who purchased the dbt Cloud Team plan before August 11, 2023, remain on a legacy pricing plan as long as your account is in good standing. The legacy pricing plan is based on seats and includes unlimited models, subject to reasonable use. 

website/docs/docs/cloud/connect-data-platform/about-connections.md

@@ -3,14 +3,15 @@ title: "About data platform connections"
 id: about-connections
 description: "Information about data platform connections"
 sidebar_label: "About data platform connections"
-pagination_next: "docs/cloud/connect-data-platform/connect-starburst-trino"
+pagination_next: "docs/cloud/connect-data-platform/connect-microsoft-fabric"
 pagination_prev: null
 ---
 dbt Cloud can connect with a variety of data platform providers including: 
 - [Amazon Redshift](/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb) 
 - [Apache Spark](/docs/cloud/connect-data-platform/connect-apache-spark) 
 - [Databricks](/docs/cloud/connect-data-platform/connect-databricks) 
 - [Google BigQuery](/docs/cloud/connect-data-platform/connect-bigquery)
+- [Microsoft Fabric](/docs/cloud/connect-data-platform/connect-microsoft-fabric)
 - [PostgreSQL](/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb)
 - [Snowflake](/docs/cloud/connect-data-platform/connect-snowflake)
 - [Starburst or Trino](/docs/cloud/connect-data-platform/connect-starburst-trino)

website/docs/docs/cloud/connect-data-platform/connect-microsoft-fabric.md

@@ -0,0 +1,43 @@
+---
+title: "Connect Microsoft Fabric"
+description: "Configure Microsoft Fabric connection."
+sidebar_label: "Connect Microsoft Fabric"
+---
+
+## Supported authentication methods
+The supported authentication methods are: 
+- Azure Active Directory (Azure AD) service principal
+- Azure AD password
+
+SQL password (LDAP) is not supported in Microsoft Fabric Synapse Data Warehouse so you must use Azure AD. This means that to use [Microsoft Fabric](https://www.microsoft.com/en-us/microsoft-fabric) in dbt Cloud, you will need at least one Azure AD service principal to connect dbt Cloud to Fabric, ideally one service principal for each user.
+
+### Active Directory service principal 
+The following are the required fields for setting up a connection with a Microsoft Fabric using Azure AD service principal authentication. 
+
+| Field | Description |
+| --- | --- |
+| **Server** | The service principal's **host** value for the Fabric test endpoint. |
+| **Port** | The port to connect to Microsoft Fabric. You can use `1433` (the default), which is the standard SQL server port number. |
+| **Database** | The service principal's **database** value for the Fabric test endpoint. |
+| **Authentication** | Choose **Service Principal** from the dropdown. | 
+| **Tenant ID** | The service principal's **Directory (tenant) ID**. |
+| **Client ID** | The service principal's **application (client) ID id**. |
+| **Client secret** | The service principal's **client secret** (not the **client secret id**). |  
+
+
+### Active Directory password 
+
+The following are the required fields for setting up a connection with a Microsoft Fabric using Azure AD password authentication. 
+
+| Field | Description |
+| --- | --- |
+| **Server** | The server hostname to connect to Microsoft Fabric. |
+| **Port** | The server port. You can use `1433` (the default), which is the standard SQL server port number. |
+| **Database** | The database name. |
+| **Authentication** | Choose **Active Directory Password** from the dropdown. | 
+| **User** | The AD username. |
+| **Password** | The AD username's password. |
+
+## Configuration 
+
+To learn how to optimize performance with data platform-specific configurations in dbt Cloud, refer to [Microsoft Fabric DWH configurations](/reference/resource-configs/fabric-configs).