App Reporting dbt Package

This dbt package unifies and aggregates data from Fivetran's Apple App Store and Google Play connectors into analytics-ready tables.

Resources

Number of materialized models¹: 96
Connector documentation
- Apple App Store
- Google Play
dbt package documentation

What does this dbt package do?

This package enables you to standardize schemas from various app platform connectors and create reporting models for all activity aggregated to the device, country, OS version, app version, and traffic source levels. It creates enriched models with metrics focused on app performance, user engagement, and platform-specific analytics.

Currently supports the following Fivetran app platform connectors:

The individual Google Play and Apple App Store tables have additional platform-specific metrics better suited for deep-dive analyses.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_app_reporting

Final output tables

By default, this package materializes the following final tables:

Table	Description
app_reporting__app_version_report	Tracks daily app performance metrics by app version to monitor version adoption, identify version-specific issues, and understand how different app versions perform. Example Analytics Questions: Which app versions have the highest user adoption and retention rates? Are there specific app versions experiencing higher crash rates or performance issues? How do key metrics (sessions, revenue, engagement) compare across different app versions?
app_reporting__country_report	Analyzes daily app performance by country to understand geographic distribution of users, revenue by region, and identify market-specific opportunities and challenges. Example Analytics Questions: Which countries generate the most app revenue and have the highest user engagement? How do key performance metrics vary across different geographic markets? What countries show the strongest growth in daily active users or revenue?
app_reporting__device_report	Monitors daily app metrics by device type to optimize device-specific experiences, identify device compatibility issues, and understand device preferences among users. Example Analytics Questions: Which device types have the highest user volumes and best performance metrics? Are there device-specific crashes or performance issues affecting user experience? How do engagement and revenue metrics differ between iOS and Android devices?
app_reporting__os_version_report	Tracks daily performance metrics by operating system version to ensure compatibility, prioritize OS version support, and identify version-specific issues. Example Analytics Questions: Which OS versions have the most active users and highest engagement rates? Are newer OS versions showing better or worse performance metrics? What percentage of users are on outdated OS versions that may need deprecation?
app_reporting__overview_report	Provides a high-level daily summary of app performance across all dimensions to monitor overall app health, track key metrics, and identify trends at the app level. Example Analytics Questions: What are the overall daily active users, sessions, and revenue trends for each app? How do total app metrics compare week-over-week or month-over-month? Which apps in the portfolio are performing best across key engagement and revenue metrics?
app_reporting__traffic_source_report	Analyzes daily app metrics by traffic source to measure acquisition channel effectiveness, optimize marketing spend, and understand which sources drive the most valuable users. Example Analytics Questions: Which traffic sources generate the most app installs and highest lifetime value users? How do organic versus paid acquisition channels compare in terms of user quality and retention? What is the cost per acquisition and return on ad spend by traffic source?

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.

Materialized Models

Each Quickstart transformation job run materializes the following model counts for each selected connector. The total model count represents all staging, intermediate, and final models, materialized as view, table, or incremental:

Connector	Model Count
App Reporting	18
Apple App Store	38
Google Play	40

Prerequisites

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

At least one Fivetran App Reporting connection syncing data into your destination.
A BigQuery, Snowflake, Redshift, Postgres, 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.