Overview
This lecture explains how to set up Continuous Integration and Continuous Deployment (CI/CD) for Databricks using Azure DevOps, covering repo setup, branching, pipelines, and deployment automation.
Prerequisites & Setup
- Ensure you have Databricks and Azure DevOps accounts with matching user IDs.
- Create a new Azure DevOps project where code will be stored and managed.
- Initialize a repository in Azure DevOps (Repos section).
- In Databricks, access Repos to integrate with Azure DevOps.
Linking Databricks Repos with Azure DevOps
- Use "Add Repo" in Databricks to link your development space with an Azure DevOps repository via the Git repository URL.
- Pull the latest code from Azure DevOps to your Databricks environment for development.
- After making changes in Databricks, commit and push code back to Azure DevOps.
Branching & Pull Requests
- Create feature branches in Azure DevOps for individual development tasks.
- Developers push changes to feature branches, then create pull requests to merge into the main branch.
- Pull requests are reviewed and approved before merging; delete feature branches after merging to avoid clutter.
CI/CD Pipeline Creation
- Create a YAML pipeline in Azure DevOps for automation.
- The pipeline process:
- Pulls the latest code and creates an artifact.
- Uses Databricks CLI for workspace operations.
- Connects to Databricks using a host URL and token (stored in Azure DevOps variable groups).
- Deletes old files from the target folder (e.g., 'release') and imports updated artifacts.
- Define triggers in the pipeline YAML to run on changes to the main branch.
Automating Deployments
- On each push to the main branch, the pipeline updates the 'release' folder in Databricks with the latest code.
- Only necessary files (e.g., .py) are included using an artifact ignore file.
- The updated release folder content can be used for job scheduling in Databricks.
Error Handling & Best Practices
- If errors occur while pushing, check the workspace settings for authorized Git repository restrictions.
- Always pull the latest code before pushing changes to avoid conflicts.
- Regularly clean up branches after merging to maintain a tidy repository.
Key Terms & Definitions
- Databricks — A cloud-based data analytics and engineering platform.
- Azure DevOps — Microsoft's suite for development collaboration, version control, and CI/CD.
- Repo (Repository) — A storage location for code and versioned files.
- Artifact — A bundle of code/files generated during pipeline execution for deployment.
- Pipeline (YAML) — An automated workflow defined as code to build, test, and deploy changes.
- Pull Request (PR) — A request to merge code from one branch into another, often reviewed by peers.
Action Items / Next Steps
- Set up Azure DevOps and Databricks accounts if not already done.
- Integrate Databricks with your Azure DevOps repository.
- Create and configure your YAML CI/CD pipeline with proper variables and triggers.
- Test code commits and monitor automated deployments to Databricks.