From 986563009acfb307ec1b71bdeb06487ec3132a33 Mon Sep 17 00:00:00 2001 From: Joe Markiewicz <74217849+fivetran-joemarkiewicz@users.noreply.github.com> Date: Thu, 5 Sep 2024 16:34:31 -0500 Subject: [PATCH] Documentation Standard Updates (#88) * MagicBot/documentation-updates * Apply suggestions from code review * Update README.md * Update README.md --------- Co-authored-by: Jamie Rodriguez <65564846+fivetran-jamie@users.noreply.github.com> --- .github/ISSUE_TEMPLATE/bug-report.yml | 23 ++++- .github/ISSUE_TEMPLATE/feature-request.yml | 11 ++- .../maintainer_pull_request_template.md | 3 +- .github/pull_request_template.md | 8 +- .quickstart/quickstart.yml | 15 ++++ README.md | 87 +++++++++---------- 6 files changed, 96 insertions(+), 51 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml index 17c2c54..38c56bc 100644 --- a/.github/ISSUE_TEMPLATE/bug-report.yml +++ b/.github/ISSUE_TEMPLATE/bug-report.yml @@ -1,7 +1,7 @@ name: šŸž Bug description: Report a bug or an issue you've found within the dbt package title: "[Bug] " -labels: ["bug", "triage"] +labels: ["type:bug"] body: - type: markdown attributes: @@ -35,6 +35,12 @@ body: description: A concise description of what you expected to happen. validations: required: true + - type: textarea + attributes: + label: Possible solution + description: Were you able to investigate and/or discover a potential fix to this bug in your investigation? If so, it would be much appreciated if you could submit code samples to show us how your fix resolved this issue. + validations: + required: false - type: textarea attributes: label: dbt Project configurations @@ -61,6 +67,19 @@ body: - other (mention it in "Additional Context") validations: required: true + - type: dropdown + id: orchestration_type + attributes: + label: How are you running this dbt package? + multiple: true + options: + - Fivetran Quickstart Data Model + - Fivetran Transformations + - dbt Coreā„¢ + - dbt Cloudā„¢ + - other (mention it in "Additional Context") + validations: + required: true - type: textarea attributes: label: dbt Version @@ -83,6 +102,6 @@ body: description: Our team will assess this issue and let you know if we will add it to a future sprint. However, if you would like to expedite the solution, we encourage you to contribute to the package via a PR. Our team will then work with you to approve and merge your contributions as soon as possible. options: - label: Yes. - - label: Yes, but I will need assistance and will schedule time during our [office hours](https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours) for guidance + - label: Yes, but I will need assistance. - label: No. required: false \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/feature-request.yml b/.github/ISSUE_TEMPLATE/feature-request.yml index a1d28bb..529e9bc 100644 --- a/.github/ISSUE_TEMPLATE/feature-request.yml +++ b/.github/ISSUE_TEMPLATE/feature-request.yml @@ -1,7 +1,7 @@ name: šŸŽ‰ Feature description: Suggest a new feature for the Fivetran dbt package title: "[Feature] <title>" -labels: ["enhancement"] +labels: ["type:enhancement"] body: - type: markdown attributes: @@ -20,6 +20,13 @@ body: description: A clear and concise description of what you want to happen and why you want the new feature. validations: required: true + - type: textarea + attributes: + label: How would you implement this feature? + description: | + How would you build out this feature with your existing data? Any code examples you can provide to help accelerate development on this issue? + validations: + required: true - type: textarea attributes: label: Describe alternatives you've considered @@ -34,7 +41,7 @@ body: description: Our team will assess this feature and let you know if we will add it to a future sprint. However, if you would like to expedite the feature, we encourage you to contribute to the package via a PR. Our team will then work with you to approve and merge your contributions as soon as possible. options: - label: Yes. - - label: Yes, but I will need assistance and will schedule time during your [office hours](https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours) for guidance. + - label: Yes, but I will need assistance. - label: No. required: false - type: textarea diff --git a/.github/PULL_REQUEST_TEMPLATE/maintainer_pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/maintainer_pull_request_template.md index 3a7126e..3220674 100644 --- a/.github/PULL_REQUEST_TEMPLATE/maintainer_pull_request_template.md +++ b/.github/PULL_REQUEST_TEMPLATE/maintainer_pull_request_template.md @@ -26,5 +26,4 @@ Please share any and all of your validation steps: ### If you had to summarize this PR in an emoji, which would it be? <!--- For a complete list of markdown compatible emojis check our this git repo (https://gist.github.com/rxaviers/7360908) --> -:dancer: - +:dancer: \ No newline at end of file diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index b4e7e8e..30849fd 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -19,7 +19,13 @@ <!--- To select a checkbox you simply need to add an "x" with no spaces between the brackets (eg. [x] Yes). --> - [ ] Yes -**Provide an emoji that best describes your current mood** +**Typically there are additional maintenance changes required before this will be ready for an upcoming release. Are you comfortable with the Fivetran team making a few commits directly to your branch?** +<!--- If you select Yes this will help expedite your PR in case there are small changes required before approval. We encourage you not to use this branch in a production environment as we may make additional updates. --> +<!--- If you select No, we will not make any changes directly to your branch and will either communicate any planned changes via the PR thread or will merge your PR into a separate branch so we may make changes without modifying your branch.. --> +- [ ] Yes +- [ ] No + +**If you had to summarize this PR in an emoji, which would it be?** <!--- For a complete list of markdown compatible emojis check our this git repo (https://gist.github.com/rxaviers/7360908) --> :dancer: diff --git a/.quickstart/quickstart.yml b/.quickstart/quickstart.yml index 275aefd..a70012e 100644 --- a/.quickstart/quickstart.yml +++ b/.quickstart/quickstart.yml @@ -12,3 +12,18 @@ destination_configurations: dispatch: - macro_namespace: dbt_utils search_order: [ 'spark_utils', 'dbt_utils' ] + +public_models: [ + "shopify__customer_email_cohorts", + "shopify__customer_cohorts", + "shopify__discounts", + "shopify__customer_emails", + "shopify__inventory_levels", + "shopify__orders", + "shopify__daily_shop", + "shopify__products", + "shopify__transactions", + "shopify__customers", + "shopify__order_lines", + "shopify__line_item_enhanced" +] diff --git a/README.md b/README.md index 90aff26..2024bfe 100644 --- a/README.md +++ b/README.md @@ -15,17 +15,17 @@ # Shopify Transformation dbt Package ([Docs](https://fivetran.github.io/dbt_shopify/)) -# šŸ“£ What does this dbt package do? +## What does this dbt package do? This package models Shopify data from [Fivetran's connector](https://fivetran.com/docs/applications/shopify). It uses data in the format described by [this ERD](https://fivetran.com/docs/applications/shopify#schemainformation) and builds off the output of our [Shopify source package](https://github.com/fivetran/dbt_shopify_source). The main focus of the package is to transform the core object tables into analytics-ready models, including a cohort model to understand how your customers are behaving over time. <!--section="shopify_transformation_model"--> -The following table provides a detailed list of all models materialized within this package by default. -> TIP: See more details about these models in the package's [dbt docs site](https://fivetran.github.io/dbt_shopify/#!/overview/shopify). +The following table provides a detailed list of all tables materialized within this package by default. +> TIP: See more details about these tables in the package's [dbt docs site](https://fivetran.github.io/dbt_shopify/#!/overview/shopify). -| **model** | **description** | +| **Table** | **Description** | | ------------------------- | ------------------------------------------------------------------------------------------------------------------ | | [shopify__customer_cohorts](https://fivetran.github.io/dbt_shopify/#!/model/model.shopify.shopify__customer_cohorts) | Each record represents the monthly performance of a customer (based on `customer_id`), including fields for the month of their 'cohort'. | | [shopify__customers](https://fivetran.github.io/dbt_shopify/#!/model/model.shopify.shopify__customers) | Each record represents a distinct `customer_id`, with additional dimensions like lifetime value and number of orders. | @@ -40,20 +40,20 @@ The following table provides a detailed list of all models materialized within t | [shopify__inventory_levels](https://fivetran.github.io/dbt_shopify/#!/model/model.shopify.shopify__inventory_levels) | Each record represents an inventory level (unique pairing of inventory items and locations), enriched with information about its products, orders, and fulfillments. | | [shopify__line_item_enhanced](https://fivetran.github.io/dbt_shopify/#!/model/model.shopify.shopify__line_item_enhanced) | This model constructs a comprehensive, denormalized analytical table that enables reporting on key revenue, customer, and product metrics from your billing platform. Itā€™s designed to align with the schema of the `*__line_item_enhanced` model found in Shopify, Recharge, Stripe, Zuora, and Recurly, offering standardized reporting across various billing platforms. To see the kinds of insights this model can generate, explore example visualizations in the [Fivetran Billing Model Streamlit App](https://fivetran-billing-model.streamlit.app/). Visit the app for more details. | -## Example Visualizations -Curious what these models can do? Check out example visualizations from the [shopify__line_item_enhanced](https://fivetran.github.io/dbt_shopify/#!/model/model.shopify.shopify__line_item_enhanced) model in the [Fivetran Billing Model Streamlit App](https://fivetran-billing-model.streamlit.app/), and see how you can use these models in your own reporting. Below is a screenshot of an example reportā€”explore the app for more. +### Example Visualizations +Curious what these tables can do? Check out example visualizations from the [shopify__line_item_enhanced](https://fivetran.github.io/dbt_shopify/#!/model/model.shopify.shopify__line_item_enhanced) table in the [Fivetran Billing Model Streamlit App](https://fivetran-billing-model.streamlit.app/), and see how you can use these tables in your own reporting. Below is a screenshot of an example reportā€”explore the app for more. <p align="center"> - <a href="https://fivetran-billing-model.streamlit.app/"> +<a href="https://fivetran-billing-model.streamlit.app/"> <img src="https://raw.githubusercontent.com/fivetran/dbt_shopify/main/images/streamlit_example.png" alt="Streamlit Billing Model App" width="75%"> - </a> +</a> </p> <!--section-end--> -# šŸŽÆ How do I use the dbt package? +## How do I use the dbt package? -## Step 1: Prerequisites +### Step 1: Prerequisites To use this dbt package, you must have the following: - At least one Fivetran Shopify connector syncing data into your destination. @@ -64,7 +64,7 @@ To use this dbt package, you must have the following: - [PostgreSQL](https://fivetran.com/docs/destinations/postgresql) - [Databricks](https://fivetran.com/docs/destinations/databricks) with [Databricks Runtime](https://docs.databricks.com/en/compute/index.html#databricks-runtime) -## Step 2: Install the package (skip if also using the `shopify_holistic_reporting` package) +### Step 2: Install the package (skip if also using the `shopify_holistic_reporting` package) If you are **not** using the [Shopify Holistic reporting package](https://github.com/fivetran/dbt_shopify_holistic_reporting), include the following shopify package version in your `packages.yml` file: > TIP: Check [dbt Hub](https://hub.getdbt.com/) for the latest installation instructions or [read the dbt docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages. ```yml @@ -73,9 +73,9 @@ packages: version: [">=0.13.0", "<0.14.0"] # we recommend using ranges to capture non-breaking changes automatically ``` -Do **NOT** include the `shopify_source` package in this file. The transformation package itself has a dependency on it and will install the source package as well. +Do **NOT** include the `shopify_source` package in this file. The transformation package itself has a dependency on it and will install the source package as well. -### Databricks dispatch configuration +#### Databricks dispatch configuration If you are using a Databricks destination with this package, you must add the following (or a variation of the following) dispatch configuration within your `dbt_project.yml`. This is required in order for the package to accurately search for macros within the `dbt-labs/spark_utils` then the `dbt-labs/dbt_utils` packages respectively. ```yml dispatch: @@ -83,8 +83,8 @@ dispatch: search_order: ['spark_utils', 'dbt_utils'] ``` -## Step 3: Define database and schema variables -### Single connector +### Step 3: Define database and schema variables +#### Single connector By default, this package runs using your destination and the `shopify` schema. If this is not where your Shopify data is (for example, if your Shopify schema is named `shopify_fivetran`), add the following configuration to your root `dbt_project.yml` file: ```yml @@ -94,7 +94,7 @@ vars: shopify_database: your_database_name shopify_schema: your_schema_name ``` -### Union multiple connectors +#### Union multiple connectors If you have multiple Shopify connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the `source_relation` column of each model. To use this functionality, you will need to set either the `shopify_union_schemas` OR `shopify_union_databases` variables (cannot do both) in your root `dbt_project.yml` file: ```yml @@ -104,15 +104,15 @@ vars: shopify_union_schemas: ['shopify_usa','shopify_canada'] # use this if the data is in different schemas/datasets of the same database/project shopify_union_databases: ['shopify_usa','shopify_canada'] # use this if the data is in different databases/projects but uses the same schema name ``` -Please be aware that the native `source.yml` connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined `source.yml`. +> NOTE: The native `source.yml` connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined `source.yml`. To connect your multiple schema/database sources to the package models, follow the steps outlined in the [Union Data Defined Sources Configuration](https://github.com/fivetran/dbt_fivetran_utils/tree/releases/v0.4.latest#union_data-source) section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG. -## Step 4: Enable `fulfillment_event` data +### Step 4: Enable `fulfillment_event` data -The package takes into consideration that not every Shopify connector may have `fulfillment_event` data enabled. However, this table does hold valuable information that is leveraged in the `shopify__daily_shop` model. `fulfillment_event` data is **disabled by default**. +The package takes into consideration that not every Shopify connector may have `fulfillment_event` data enabled. However, this table does hold valuable information that is leveraged in the `shopify__daily_shop` model. `fulfillment_event` data is **disabled by default**. -Add the following variable to your `dbt_project.yml` file to enable the modeling of fulfillment events: +Add the following variable to your `dbt_project.yml` file to enable the modeling of fulfillment events: ```yml # dbt_project.yml @@ -120,8 +120,8 @@ vars: shopify_using_fulfillment_event: true # false by default ``` -## Step 5: Setting your timezone -By default, the data in your Shopify schema is in UTC. However, you may want reporting to reflect a specific timezone for more realistic analysis or data validation. +### Step 5: Setting your timezone +By default, the data in your Shopify schema is in UTC. However, you may want reporting to reflect a specific timezone for more realistic analysis or data validation. To convert the timezone of **all** timestamps in the package, update the `shopify_timezone` variable to your target zone in [IANA tz Database format](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones): ```yml @@ -131,12 +131,12 @@ vars: shopify_timezone: "America/New_York" # Replace with your timezone ``` -> **Note**: This will only **numerically** convert timestamps to your target timezone. They will however have a "UTC" appended to them. This is a current limitation of the dbt-date `convert_timezone` [macro](https://github.com/calogica/dbt-date#convert_timezone-column-target_tznone-source_tznone) we leverage. +> **Note**: This will only **numerically** convert timestamps to your target timezone. They will however have a "UTC" appended to them. This is a current limitation of the dbt-date `convert_timezone` [macro](https://github.com/calogica/dbt-date#convert_timezone-column-target_tznone-source_tznone) we leverage. -## (Optional) Step 6: Additional configurations +### (Optional) Step 6: Additional configurations <details open><summary>Expand/Collapse details</summary> -### Enabling Standardized Billing Model +#### Enabling Standardized Billing Model This package contains the `shopify__line_item_enhanced` model which constructs a comprehensive, denormalized analytical table that enables reporting on key revenue, subscription, customer, and product metrics from your billing platform. Itā€™s designed to align with the schema of the `*__line_item_enhanced` model found in Recurly, Recharge, Stripe, Shopify, and Zuora, offering standardized reporting across various billing platforms. To see the kinds of insights this model can generate, explore example visualizations in the [Fivetran Billing Model Streamlit App](https://fivetran-billing-model.streamlit.app/). For the time being, this model is disabled by default. If you would like to enable this model you will need to adjust the `shopify__standardized_billing_model_enabled` variable to be `true` within your `dbt_project.yml`: ```yml @@ -144,7 +144,7 @@ vars: shopify__standardized_billing_model_enabled: true # false by default. ``` -### Passing Through Additional Fields +#### Passing Through Additional Fields This package includes all source columns defined in the macros folder. You can add more columns using our pass-through column variables. These variables allow for the pass-through fields to be aliased (`alias`) and casted (`transform_sql`) if desired, but not required. Datatype casting is configured via a sql snippet within the `transform_sql` key. You may add the desired sql while omitting the `as field_name` at the end and your custom pass-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables: ```yml @@ -171,9 +171,9 @@ vars: alias: "custom_field" ``` -### Adding Metafields +#### Adding Metafields In [May 2021](https://fivetran.com/docs/applications/shopify/changelog#may2021) the Shopify connector included support for the [metafield resource](https://shopify.dev/api/admin-rest/2023-01/resources/metafield). If you would like to take advantage of these metafields, this package offers corresponding mapping models which append these metafields to the respective source object for the following tables: collection, customer, order, product_image, product, product_variant, shop. If enabled, these models will materialize as `shopify__[object]_metafields` for each respective supported object. To enable these metafield mapping models, you may use the following configurations within your `dbt_project.yml`. ->**Note**: These metafield models will contain all the same records as the corresponding staging models with the exception of the metafield columns being added. +>**Note**: These metafield models will contain all the same records as the corresponding staging models with the exception of the metafield columns being added. ```yml vars: @@ -187,7 +187,7 @@ vars: shopify_using_shop_metafields: True ## False by default. Will enable ONLY the shop metafield model. ``` -### Changing the Build Schema +#### Changing the Build Schema By default this package will build the Shopify staging models within a schema titled (<target_schema> + `_stg_shopify`) and the Shopify final models within a schema titled (<target_schema> + `_shopify`) in your target database. If this is not where you would like your modeled Shopify data to be written to, add the following configuration to your `dbt_project.yml` file: ```yml @@ -200,7 +200,7 @@ models: +schema: my_new_schema_name # leave blank for just the target_schema ``` -### Change the source table references +#### Change the source table references If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable: > IMPORTANT: See this project's [`dbt_project.yml`](https://github.com/fivetran/dbt_shopify_source/blob/main/dbt_project.yml) variable declarations to see the expected names. @@ -212,7 +212,7 @@ vars: shopify_<default_source_table_name>_identifier: your_table_name ``` -#### Lookback Window +##### Lookback Window Records from the source can sometimes arrive late. Since several of the models in this package are incremental, by default we look back 7 days to ensure late arrivals are captured while avoiding the need for frequent full refreshes. While the frequency can be reduced, we still recommend running `dbt --full-refresh` periodically to maintain data quality of the models. For more information on our incremental decisions, see the [Incremental Strategy section](https://github.com/fivetran/dbt_shopify/blob/main/DECISIONLOG.md#incremental-strategy) of the DECISIONLOG. To change the default lookback window, add the following variable to your `dbt_project.yml` file: @@ -223,8 +223,8 @@ vars: lookback_window: number_of_days # default is 7 ``` -#### Change the calendar start date -Our date-based models start at `2019-01-01` by default. To customize the start date, add the following variable to your `dbt_project.yml` file: +##### Change the calendar start date +Our date-based models start at `2019-01-01` by default. To customize the start date, add the following variable to your `dbt_project.yml` file: ```yml vars: @@ -235,7 +235,7 @@ vars: </details> -## (Optional) Step 7: Orchestrate your models with Fivetran Transformations for dbt Coreā„¢ +### (Optional) Step 7: Orchestrate your models with Fivetran Transformations for dbt Coreā„¢ <details><summary>Expand for details</summary> <br> @@ -243,8 +243,8 @@ Fivetran offers the ability for you to orchestrate your dbt project through [Fiv </details> -# šŸ” Does this package have dependencies? -This dbt package is dependent on the following dbt packages. Please be aware that these dependencies are installed by default within this package. For more information on the following packages, refer to the [dbt hub](https://hub.getdbt.com/) site. +## Does this package have dependencies? +This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the [dbt hub](https://hub.getdbt.com/) site. > IMPORTANT: If you have any of these dependent packages in your own `packages.yml` file, we highly recommend that you remove them from your root `packages.yml` to avoid package version conflicts. ```yml @@ -264,16 +264,15 @@ packages: - package: dbt-labs/spark_utils version: [">=0.3.0", "<0.4.0"] ``` -# šŸ™Œ How is this package maintained and can I contribute? -## Package Maintenance +## How is this package maintained and can I contribute? +### Package Maintenance The Fivetran team maintaining this package _only_ maintains the latest version of the package. We highly recommend you stay consistent with the [latest version](https://hub.getdbt.com/fivetran/shopify/latest/) of the package and refer to the [CHANGELOG](https://github.com/fivetran/dbt_shopify/blob/main/CHANGELOG.md) and release notes for more information on changes across versions. -## Contributions -A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions! +### Contributions +A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions. -We highly encourage and welcome contributions to this package. Check out [this dbt Discourse article](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) on the best workflow for contributing to a package! +We highly encourage and welcome contributions to this package. Check out [this dbt Discourse article](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) on the best workflow for contributing to a package. -# šŸŖ Are there any resources available? -- If you have questions or want to reach out for help, please refer to the [GitHub Issue](https://github.com/fivetran/dbt_shopify/issues/new/choose) section to find the right avenue of support for you. +## Are there any resources available? +- If you have questions or want to reach out for help, see the [GitHub Issue](https://github.com/fivetran/dbt_shopify/issues/new/choose) section to find the right avenue of support for you. - If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our [Feedback Form](https://www.surveymonkey.com/r/DQ7K7WW). -- Have questions or want to be part of the community discourse? Create a post in the [Fivetran community](https://community.fivetran.com/t5/user-group-for-dbt/gh-p/dbt-user-group) and our team along with the community can join in on the discussion!