Apple Store dbt Package
This dbt package transforms data from Fivetran's Apple Store connector into analytics-ready tables.
Resources
- Number of materialized models¹: 38
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to better understand your Apple App Store metrics at different granularities and provides intuitive reporting at the App Version, Platform Version, Device, Source Type, Territory, Subscription and Overview levels. It creates enriched models with metrics focused on aggregating all relevant application metrics into each of the reporting levels.
Output schema
Final output tables are generated in the following target schema:
<your_database>.<connector/schema_name>_apple_store
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| apple_store__app_version_report | Tracks daily App Store metrics by app version and source type including crashes, active devices, installations, deletions, and sessions to monitor version performance and identify version-specific issues. Example Analytics Questions:
|
| apple_store__device_report | Analyzes daily App Store metrics by device type and source including downloads, crashes, impressions, sessions, and subscription counts across different subscription types to optimize device-specific experiences. Example Analytics Questions:
|
| apple_store__overview_report | Provides a comprehensive daily summary of App Store performance including downloads, active devices, sessions, crashes, page views, and subscription counts across all subscription types to monitor overall app health. Example Analytics Questions:
|
| apple_store__platform_version_report | Monitors daily App Store metrics by platform version and source type including downloads, crashes, impressions, active devices, and sessions to ensure platform compatibility and prioritize platform version support. Example Analytics Questions:
|
| apple_store__source_type_report | Analyzes daily App Store performance by acquisition source type including downloads, impressions, page views, active devices, sessions, installations, and deletions to measure channel effectiveness and optimize marketing spend. Example Analytics Questions:
|
| apple_store__subscription_report | Tracks daily subscription counts by product, territory, and state across different subscription types (free trial, pay-as-you-go, pay-up-front, standard) to analyze subscription performance and geographic distribution. Example Analytics Questions:
|
| apple_store__territory_report | Monitors daily App Store metrics by territory and source type including downloads, impressions, page views, active devices, sessions, installations, and deletions to understand regional performance and optimize regional marketing. 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 Apple Store 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.
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.