GitHub dbt Package (Docs)

What does this dbt package do?

• Produces modeled tables that leverage GitHub data from Fivetran's connector in the format described by this ERD.
• Provides insight into GitHub issues and pull requests by enhancing these core objects with commonly used metrics.
• Produces metrics tables, which increase understanding of your team's velocity over time. Metrics are available on a daily, weekly, monthly, and quarterly level.
• Generates a comprehensive data dictionary of your source and modeled github data through the dbt docs site.

The following table provides a detailed list of all tables materialized within this package by default.

TIP: See more details about these tables in the package's dbt docs site.

Table	Description
github__issues	Each record represents a GitHub issue, enriched with data about its assignees, milestones, and time comparisons.
github__pull_requests	Each record represents a GitHub pull request, enriched with data about its repository, reviewers, and durations between review requests, merges and reviews.
github__daily_metrics	Each record represents a single day and repository, enriched with metrics about PRs and issues that were created and closed during that period.
github__weekly_metrics	Each record represents a single week and repository, enriched with metrics about PRs and issues that were created and closed during that period.
github__monthly_metrics	Each record represents a single month and repository, enriched with metrics about PRs and issues that were created and closed during that period.
github__quarterly_metrics	Each record represents a single quarter and repository, enriched with metrics about PRs and issues that were created and closed during that period.

Materialized Models

Each Quickstart transformation job run materializes 34 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.

How do I use the dbt package?

Step 1: 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.

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']

Step 2: 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: [">=1.1.0", "<1.2.0"] # 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.

Step 3: 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 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 Step 4), 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

Step 4: 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) Step 5: 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) Step 6: 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. Check out this dbt Discourse article on the best workflow for contributing to a package.

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.