Google Play Transformation dbt Package (Docs)

What does this dbt package do?

Produces modeled tables that leverage Google Play data from Fivetran's connector in the format described by this ERD and build off the output of our Google Play source package.
Enables you to better understand your Google Play app performance metrics at different granularities. It achieves this by:
- Providing intuitive reporting at the App Version, OS Version, Device Type, Country, Overview, and Product (Subscription + In-App Purchase) levels
- Aggregates all relevant application metrics into each of the reporting levels above
Generates a comprehensive data dictionary of your source and modeled Google Play data through the dbt docs site.

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.

Table	Description
google_play__app_version_report	Each record represents daily installs, crashes and ANRs, and ratings by app version and app.
google_play__country_report	Each record represents daily installs, ratings, and store performance metrics by user country and app.
google_play__device_report	Each record represents daily installs and ratings by device model type and app.
google_play__os_version_report	Each record represents daily installs, crashes and ANRs, and ratings by android os version and app.
google_play__overview_report	Each record represents daily installs, crashes and ANRs, store performance metrics, and ratings by app.
google_play__finance_report	Each record represents daily subscriptions, purchases, and different kinds of revenue by product and country.

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, or Databricks destination.

Step 2: Install the package (skip if also using the `app_reporting` transformation package)

Include the following Google Play 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
    version: [">=0.4.0", "<0.5.0"] # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the google_play_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.

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 or enable source tables

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) and your Google Play modeling models within a schema titled (<target_schema> + _google_play) in your destination. If this is not where you would like your Google Play 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
    google_play:
      +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 for 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/google_play_source
      version: [">=0.4.0", "<0.5.0"]

    - 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 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 on the best workflow for contributing to a 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.