- Materializes Apple App Store staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Apple App Store data from Fivetran’s connector for analysis by doing the following:
- Name columns for consistency across all packages and for easier analysis
- Adds freshness tests to source data
- Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
- Generates a comprehensive data dictionary of your Apple App Store data through the dbt docs site.
- These tables are designed to work simultaneously with our Apple App Store transformation package.
Step 1: Prerequisiteslink
To use this dbt package, you must have the following:
- At least one Fivetran Apple App Store connector syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
Step 2: Install the packagelink
Include the following apple_store_source package version in your
packages: - package: fivetran/apple_store_source version: [">=0.1.0", "<0.2.0"]
Step 3: Define database and schema variableslink
By default, this package runs using your destination and the
apple_store schema. If this is not where your apple_store data is (for example, if your apple_store schema is named
apple_store_fivetran), add the following configuration to your root
vars: apple_store_database: your_destination_name apple_store_schema: your_schema_name
Step 3: Enabling additional modelslink
Your Apple App Store connector may not sync every table that this package expects. If you use subscriptions and have the
sales_subscription_summary tables synced, add the following variable to your
vars: apple_store__using_subscriptions: true # by default this is assumed to be false
(Optional) Step 4: Additional configurationslinkExpand to view configurations
Defining subscription eventslink
Cancel subscription events are included and required in this package for downstream usage. If you would like to add additional subscription events, please add the below to your
apple_store__subscription_events: - 'Renew' - 'Cancel' - 'Subscribe' - '<additional_event_name>' - '<additional_event_name>'
Change the build schemalink
By default, this package builds the Apple App Store staging models within a schema titled (<target_schema> +
_apple_store_source) in your target database. If this is not where you would like your Apple App Store staging data to be written to, add the following configuration to your root
models: apple_store_source: +schema: my_new_schema_name # leave blank for just the target_schema
Change the source table referenceslink
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.ymlvariable declarations to see the expected names.
vars: <default_source_table_name>_identifier: your_table_name
(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™linkExpand to view details
Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core™ setup guides.
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 site.
IMPORTANT: If you have any of these dependent packages in your own
packages.ymlfile, we highly recommend that you remove them from your root
packages.ymlto avoid package version conflicts.
packages: - package: fivetran/fivetran_utils version: [">=0.3.0", "<0.4.0"] - package: dbt-labs/dbt_utils version: [">=0.8.0", "<0.9.0"]
The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend that you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.
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 to learn how to contribute to a dbt package!
- If you have questions or want to reach out for help, please refer to the GitHub Issue 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.
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