Updated November 1, 2023
📣 What does this dbt package do?link
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 models materialized within this package by default.
TIP: See more details about these models in the package's dbt docs site.
|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.|
🎯 How do I use the dbt package?link
Step 1: Prerequisiteslink
To use this dbt package, you must have the following:
- At least one Fivetran Github connector syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
Databricks Dispatch Configurationlink
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']content_copy
Step 2: Install the packagelink
Include the following github package version in your
packages: - package: fivetran/github version: [">=0.7.0", "<0.8.0"] # we recommend using ranges to capture non-breaking changes automaticallycontent_copy
Do NOT include the
github_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.
Step 3: Define database and schema variableslink
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
vars: github_source: github_database: your_database_name github_schema: your_schema_namecontent_copy
Step 4: Disable models for non-existent sourceslink
Your Github connector 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
REPO_TEAM table synced, add the following variable to your
vars: github__using_repo_team: false # by default this is assumed to be truecontent_copy
Note: This package only integrates the above variable. If you'd like to disable other models, please create an issue specifying which ones.
(Optional) Step 5: Additional configurationslink
Expand for configurations
Change the build schemalink
By default, this package builds the Github staging models within a schema titled (
_stg_github) and your Github modeling models within a schema titled (
_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
models: github_source: +schema: my_new_schema_name # leave blank for just the target_schema github: +schema: my_new_schema_name # leave blank for just the target_schemacontent_copy
Change the source table referenceslink
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.ymlvariable declarations to see the expected names.
vars: github_<default_source_table_name>_identifier: your_table_namecontent_copy
(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™link
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?link
This dbt package is dependent on the following dbt packages. Please be aware that 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.ymlfile, we highly recommend that you remove them from your root
packages.ymlto 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: fivetran/github_source version: [">=0.7.0", "<0.8.0"] - package: dbt-labs/spark_utils version: [">=0.3.0", "<0.4.0"]content_copy
🙌 How is this package maintained and can I contribute?link
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.
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?link
- If you have questions or want to reach out for help, please refer to 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.
- Have questions or want to be part of the community discourse? Create a post in the Fivetran community and our team along with the community can join in on the discussion!