Google Play Source dbt Package (Docs)

What does this dbt package do?

• Materializes Google Play staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Google Play data from Fivetran's connector for analysis by doing the following:
  • Name columns for consistency across all packages and 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 Google Play data through the dbt docs site.
• These tables are designed to work simultaneously with our Google Play transformation package.

How do I use the dbt package?

Step 1: Prerequisites

To use this dbt package, you must have the following:

• At least one Fivetran Google Play connector syncing data into your destination.
• A BigQuery, Snowflake, Redshift, PostgreSQL, Databricks destination.

Step 2: Install the package (skip if also using the google_play or app_reporting transformation packages)

Include the following google_play_source package version in your packages.yml file.

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.

packages:
  - package: fivetran/google_play_source
    version: [">=0.4.0", "<0.5.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define database and schema variables

By default, this package runs using your destination and the google_play schema. If this is not where your google_play data is (for example, if your google_play schema is named google_play_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    google_play_database: your_destination_name
    google_play_schema: your_schema_name 

Step 4: Disable models for non-existent sources

Your Google Play connector might not sync every table that this package expects. If you have financial and/or subscriptions data, namely the earnings and financial_stats_subscriptions_country tables, add the following variable(s) to your dbt_project.yml file:

vars:
    google_play__using_earnings: true # by default this is assumed to be FALSE
    google_play__using_subscriptions: true # by default this is assumed to be FALSE

Step 5: Seed country_codes mapping table (once)

In order to map longform territory names to their ISO country codes, we have adapted the CSV from lukes/ISO-3166-Countries-with-Regional-Codes to align Google and Apple's country name formats for the App Reporting combo package.

You will need to dbt seed the google_play__country_codes file just once.

(Optional) Step 6: Additional configurations

Expand/collapse configurations

Union multiple connectors

If you have multiple google_play 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 google_play_union_schemas OR google_play_union_databases variables (cannot do both) in your root dbt_project.yml file:

vars:
    google_play_union_schemas: ['google_play_usa','google_play_canada'] # use this if the data is in different schemas/datasets of the same database/project
    google_play_union_databases: ['google_play_usa','google_play_canada'] # use this if the data is in different databases/projects but uses the same schema name

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 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.

Change the build schema

By default, this package builds the google_play staging models within a schema titled (<target_schema> + _google_play_source) in your destination. If this is not where you would like your google_play staging data to be written to, add the following configuration to your root dbt_project.yml file:

models:
    google_play_source:
      +schema: my_new_schema_name # leave blank for just the target_schema

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 variable declarations to see the expected names.

vars:
    google_play_<default_source_table_name>_identifier: your_table_name 

(Optional) Step 7: Orchestrate your models with Fivetran Transformations for dbt Core™

Expand 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.

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 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.

packages:
    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

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 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.

Opinionated Decisions

In creating this package, which is meant for a wide range of use cases, we had to take opinionated stances on a few different questions we came across during development. We've consolidated significant choices we made in the DECISIONLOG.md, and will continue to update as the package evolves. We are always open to and encourage feedback on these choices, and the package in general.

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 to learn how to contribute to a dbt package.

Are there any resources available?

• If you have questions or want to reach out for help, see 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.