GitHub Actions

2025-06-23

Important

GitHub Actions can be used to trigger runs of your CI/CD workflows from your GitHub repositories and allows you to automate your build, test, and deployment CI/CD pipeline.

This article provides information about the GitHub Actions developed by Databricks and examples for common use cases. For information about other CI/CD features and best practices on Databricks, see CI/CD on Azure Databricks and Best practices and recommended CI/CD workflows on Databricks.

Databricks GitHub Actions

Databricks has developed the following GitHub Actions for your CI/CD workflows on GitHub. Add GitHub Actions YAML files to your repo's .github/workflows directory.

Note

This article covers GitHub Actions, which is developed by a third party. To contact the provider, see GitHub Actions Support.

GitHub Action	Description
databricks/setup-cli	A composite action that sets up the Databricks CLI in a GitHub Actions workflow.

Run a CI/CD workflow that updates a Production Git folder

The following example GitHub Actions YAML file updates a workspace Git folder when a remote branch updates. For information about the Production Git folder approach for CI/CD, see Production Git folder.

This example uses workload identity federation for GitHub Actions for enhanced security, and requires that you first follow the steps in Enable workload identity federation for GitHub Actions to create a federation policy.

name: Sync Git Folder

concurrency: prod_environment

on:
  push:
    branches:
      # Set your base branch name here
      - git-folder-cicd-example

permissions:
  id-token: write
  contents: read

jobs:
  deploy:
    runs-on: ubuntu-latest
    name: 'Update git folder'
    environment: Prod
    env:
      DATABRICKS_AUTH_TYPE: github-oidc
      DATABRICKS_HOST: ${{ vars.DATABRICKS_HOST }}
      DATABRICKS_CLIENT_ID: ${{ secrets.DATABRICKS_CLIENT_ID }}

    steps:
      - uses: actions/checkout@v3
      - uses: databricks/setup-cli@main
      - name: Update git folder
        # Set your workspace path and branch name here
        run: databricks repos update /Workspace/<git-folder-path> --branch git-folder-cicd-example

Run a CI/CD workflow with a bundle that runs a pipeline update

The following example GitHub Actions YAML file triggers a test deployment that validates, deploys, and runs the specified job in the bundle within a pre-production target named “dev” as defined within a bundle configuration file.

This example requires that there is:

A bundle configuration file at the root of the repository, which is explicitly declared through the GitHub Actions YAML file's setting working-directory: . This bundle configuration file should define an Azure Databricks workflow named my-job and a target named dev. See Databricks Asset Bundle configuration.
A GitHub secret named SP_TOKEN, representing the Azure Databricks access token for an Azure Databricks service principal that is associated with the Azure Databricks workspace to which this bundle is being deployed and run. See Encrypted secrets.

# This workflow validates, deploys, and runs the specified bundle
# within a pre-production target named "dev".
name: 'Dev deployment'

# Ensure that only a single job or workflow using the same concurrency group
# runs at a time.
concurrency: 1

# Trigger this workflow whenever a pull request is opened against the repo's
# main branch or an existing pull request's head branch is updated.
on:
  pull_request:
    types:
      - opened
      - synchronize
    branches:
      - main

jobs:
  # Used by the "pipeline_update" job to deploy the bundle.
  # Bundle validation is automatically performed as part of this deployment.
  # If validation fails, this workflow fails.
  deploy:
    name: 'Deploy bundle'
    runs-on: ubuntu-latest

    steps:
      # Check out this repo, so that this workflow can access it.
      - uses: actions/checkout@v3

      # Download the Databricks CLI.
      # See https://github.com/databricks/setup-cli
      - uses: databricks/setup-cli@main

      # Deploy the bundle to the "dev" target as defined
      # in the bundle's settings file.
      - run: databricks bundle deploy
        working-directory: .
        env:
          DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
          DATABRICKS_BUNDLE_ENV: dev

  # Validate, deploy, and then run the bundle.
  pipeline_update:
    name: 'Run pipeline update'
    runs-on: ubuntu-latest

    # Run the "deploy" job first.
    needs:
      - deploy

    steps:
      # Check out this repo, so that this workflow can access it.
      - uses: actions/checkout@v3

      # Use the downloaded Databricks CLI.
      - uses: databricks/setup-cli@main

      # Run the Databricks workflow named "my-job" as defined in the
      # bundle that was just deployed.
      - run: databricks bundle run my-job --refresh-all
        working-directory: .
        env:
          DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
          DATABRICKS_BUNDLE_ENV: dev

You may also want to trigger production deployments. The following GitHub Actions YAML file can exist in the same repo as the preceding file. This file validates, deploys, and runs the specified bundle within a production target named “prod” as defined within a bundle configuration file.

# This workflow validates, deploys, and runs the specified bundle
# within a production target named "prod".
name: 'Production deployment'

# Ensure that only a single job or workflow using the same concurrency group
# runs at a time.
concurrency: 1

# Trigger this workflow whenever a pull request is pushed to the repo's
# main branch.
on:
  push:
    branches:
      - main

jobs:
  deploy:
    name: 'Deploy bundle'
    runs-on: ubuntu-latest

    steps:
      # Check out this repo, so that this workflow can access it.
      - uses: actions/checkout@v3

      # Download the Databricks CLI.
      # See https://github.com/databricks/setup-cli
      - uses: databricks/setup-cli@main

      # Deploy the bundle to the "prod" target as defined
      # in the bundle's settings file.
      - run: databricks bundle deploy
        working-directory: .
        env:
          DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
          DATABRICKS_BUNDLE_ENV: prod

  # Validate, deploy, and then run the bundle.
  pipeline_update:
    name: 'Run pipeline update'
    runs-on: ubuntu-latest

    # Run the "deploy" job first.
    needs:
      - deploy

    steps:
      # Check out this repo, so that this workflow can access it.
      - uses: actions/checkout@v3

      # Use the downloaded Databricks CLI.
      - uses: databricks/setup-cli@main

      # Run the Databricks workflow named "my-job" as defined in the
      # bundle that was just deployed.
      - run: databricks bundle run my-job --refresh-all
        working-directory: .
        env:
          DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
          DATABRICKS_BUNDLE_ENV: prod

Run a CI/CD workflow that builds a JAR and deploys a bundle

If you have a Java-based ecosystem, your GitHub Action will need to build and upload a JAR before deploying the bundle. The following example GitHub Actions YAML file triggers a deployment that builds and uploads a JAR to a volume, then validates and deploys the bundle to a production target named "prod" as defined within the bundle configuration file. It compiles a Java-based JAR, but the compilation steps for a Scala-based project are similar.