Google Play dbt Package
This dbt package transforms data from Fivetran's Google Play connector into analytics-ready tables.
Resources
- Number of materialized models¹: 40
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to better understand your Google Play app performance metrics at different granularities and aggregate all relevant application metrics. It creates enriched models with metrics focused on App Version, OS Version, Device Type, Country, Overview, and Product (Subscription + In-App Purchase) reporting levels.
Output schema
Final output tables are generated in the following target schema:
<your_database>.<connector/schema_name>_google_play
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| google_play__app_version_report | Tracks daily installs, crashes, and user ratings by app version to monitor version stability, adoption rates, and quality. Example Analytics Questions:
|
| google_play__country_report | Analyzes daily app installs, ratings, and store visibility by country to understand geographic market performance and optimize regional app store strategies. Example Analytics Questions:
|
| google_play__device_report | Monitors daily installs and user ratings by device model to identify popular devices among users and optimize for device-specific compatibility. Example Analytics Questions:
|
| google_play__os_version_report | Analyzes daily installs, crashes, and ratings by Android OS version to prioritize OS support, identify version-specific stability issues, and understand OS adoption among users. Example Analytics Questions:
|
| google_play__overview_report | Provides a comprehensive daily overview of app performance including installs, crashes, store metrics, and ratings to monitor overall app health and user satisfaction. Example Analytics Questions:
|
| google_play__finance_report | Tracks daily subscription revenue, in-app purchases, and financial performance by product and country to analyze monetization effectiveness and revenue trends. Example Analytics Questions:
|
¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.
Prerequisites
To use this dbt package, you must have the following:
- At least one Fivetran Google Play connection syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
How do I use the dbt package?
You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:
- To add the package in the Fivetran dashboard, follow our Quickstart guide.
- To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.
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.
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. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.