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
  • Github connector documentation
  • Github ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog

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: Which issues have been open the longest and which contributors are assigned to them? What is the average time to close issues by label or milestone? How many comments and reactions do issues typically receive before being resolved?
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: What is the average time from PR creation to merge by repository or contributor? Which pull requests have the most review cycles or requested changes before approval? How many comments are typically exchanged per pull request?
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: How many pull requests and issues are opened versus closed each day by repository? What is the daily velocity of code changes and issue resolution? Are there daily patterns in development activity that could inform sprint planning?
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: What is the weekly throughput of pull requests and issues by repository? How do weekly development metrics trend over time? Which weeks show the highest productivity in terms of PRs merged and issues resolved?
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: How do monthly pull request and issue volumes trend across repositories? What is the month-over-month growth in development activity and code contributions? Which months show the highest productivity and how does this align with roadmap milestones?
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: What is the quarterly velocity of feature development and issue resolution by repository? How do quarterly metrics align with product roadmap goals and release cycles? Which quarters show the strongest team performance and code contribution activity?

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.

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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.

Install the package

Include the following github 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/github
    version: [">=1.3.0", "<1.4.0"] # we recommend using ranges to capture non-breaking changes automatically

All required sources and staging models are now bundled into this transformation package. Do not include fivetran/github_source in your packages.yml since this package has been deprecated.

Databricks Dispatch Configuration

If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Define database and schema variables

Option A: Single connection

By default, this package runs using your destination and the github schema. If this is not where your GitHub data is (for example, if your github schema is named github_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
  github:
    github_database: your_database_name
    github_schema: your_schema_name

Option B: Union multiple connections

If you have multiple GitHub connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. For each source table, the package will union all of the data together and pass the unioned table into the transformations. The source_relation column in each model indicates the origin of each record.

To use this functionality, you will need to set the github_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  github:
    github_sources:
      - database: connection_1_destination_name # Required
        schema: connection_1_schema_name # Required
        name: connection_1_source_name # Required only if following the step in the following subsection

      - database: connection_2_destination_name
        schema: connection_2_schema_name
        name: connection_2_source_name

Recommended: Incorporate unioned sources into DAG

If you are running the package through Fivetran Transformations for dbt Core™, the below step is necessary in order to synchronize model runs with your GitHub connections. Alternatively, you may choose to run the package through Fivetran Quickstart, which would create separate sets of models for each GitHub source rather than one set of unioned models.

By default, this package defines one single-connection source, called github, which will be disabled if you are unioning multiple connections. This means that your DAG will not include your GitHub sources, though the package will run successfully.

To properly incorporate all of your GitHub connections into your project's DAG:

1. Define each of your sources in a .yml file in the models directory of your project. Utilize the following template for the source-level configurations, and, most importantly, copy and paste the table and column-level definitions from the package's src_github.yml file.

# a .yml file in your root project

version: 2

sources:
  - name: <name> # ex: Should match name in github_sources
    schema: <schema_name>
    database: <database_name>
    loader: fivetran
    config:
      loaded_at_field: _fivetran_synced
      freshness: # feel free to adjust to your liking
        warn_after: {count: 72, period: hour}
        error_after: {count: 168, period: hour}

    tables: # copy and paste from github/models/staging/src_github.yml - see https://support.atlassian.com/bitbucket-cloud/docs/yaml-anchors/ for how to use anchors to only do so once

Note: If there are source tables you do not have (see Disable models for non-existent sources), you may still include them, as long as you have set the right variables to False.

2. Set the has_defined_sources variable (scoped to the github package) to True, like such:

# dbt_project.yml
vars:
  github:
    has_defined_sources: true

Disable models for non-existent sources

Your GitHub connection might not sync every table that this package expects. If your syncs exclude certain tables, it is because you either don't use that functionality in GitHub or have actively excluded some tables from your syncs.

If you do not have the TEAM, REPO_TEAM, ISSUE_ASSIGNEE, ISSUE_LABEL, LABEL, or REQUESTED_REVIEWER_HISTORY tables synced and are not running the package via Fivetran Quickstart, add the following variables to your dbt_project.yml file:

vars:
    github__using_repo_team: false # by default this is assumed to be true. Set to false if missing TEAM or REPO_TEAM
    github__using_issue_assignee: false # by default this is assumed to be true
    github__using_issue_label: false # by default this is assumed to be true
    github__using_label: false # by default this is assumed to be true
    github__using_requested_reviewer_history: false # by default this is assumed to be true

Note: This package only integrates the above variables. If you'd like to disable other models, please create an issue specifying which ones.

(Optional) Additional configurations

Expand/collapse configurations

Change the build schema

By default, this package builds the GitHub staging models within a schema titled (<target_schema> + _github_source) and your GitHub modeling models within a schema titled (<target_schema> + _github) in your destination. If this is not where you would like your GitHub data to be written to, add the following configuration to your root dbt_project.yml file:

models:
    github:
      +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.
      staging:
        +schema: my_new_schema_name # Leave +schema: blank to use the default 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:
    github_<default_source_table_name>_identifier: your_table_name 

(Optional) Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for more 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/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

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.

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.

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.