Github dbt Package
This dbt package transforms data from Fivetran's Github connector into analytics-ready tables.
Resources
- Number of materialized models¹: 34
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to analyze GitHub issues and pull requests, enhance core objects with commonly used metrics, and produce velocity metrics over time. It creates enriched models with metrics focused on issue and pull request tracking, team performance, and repository activity.
Output schema
Final output tables are generated in the following target schema:
<your_database>.<connector/schema_name>_github
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| github__issues | Tracks all GitHub issues with creator information, labels, lifecycle metrics, and comment activity to monitor issue resolution times, contributor engagement, and project health. Example Analytics Questions:
|
| github__pull_requests | Provides comprehensive pull request data including reviewers, approval status, merge times, changed files, and review cycles to analyze code review efficiency and development velocity. Example Analytics Questions:
|
| github__daily_metrics | Tracks daily repository activity including pull requests and issues created and closed to monitor development velocity and project health on a day-by-day basis. Example Analytics Questions:
|
| github__weekly_metrics | Aggregates weekly repository activity to analyze sprint-level productivity, track week-over-week trends, and understand development patterns at the weekly cadence. Example Analytics Questions:
|
| github__monthly_metrics | Summarizes monthly repository activity to track long-term development trends, measure team productivity over time, and identify seasonal patterns in contribution activity. Example Analytics Questions:
|
| github__quarterly_metrics | Provides quarterly repository performance metrics to support strategic planning, measure progress against OKRs, and understand high-level development trends by quarter. 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 Github 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.