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PySparkSnowflakeDatabricks

Repos & CI/CD — Git Integration and Code Promotion

🎬 Story Time — “We Need Version Control on Notebooks”​

Meera, a data engineer, faces recurring issues:

  • Developers overwrite each other’s notebooks
  • Production jobs break after untested changes
  • Difficult to track who changed what

“We need Git integration and proper CI/CD for Databricks,” she thinks.

Enter Databricks Repos + CI/CD pipelines — a seamless solution to version control, collaboration, and deployment.

🔥 1. What Are Databricks Repos?​

Databricks Repos allow teams to:

  • Clone Git repositories into Databricks
  • Edit notebooks, scripts, and SQL directly
  • Commit and push changes to Git
  • Work collaboratively without breaking production

Supports:

  • GitHub
  • GitLab
  • Bitbucket
  • Azure DevOps

🧱 2. Key Benefits​

Collaboration & Version Control​

  • Track notebook versions
  • Revert changes easily
  • Branching for feature development

Production Safety​

  • Separate dev/test/prod branches
  • CI/CD pipelines for automated deployments

Enterprise Governance​

  • Audit changes
  • Role-based access
  • Integrate with CI/CD tools

⚙️ 3. Setting Up Databricks Repos​

  1. Go to Repos → Add Repo → Git Provider
  2. Authenticate via token or OAuth
  3. Clone the repo into Databricks workspace

/Repos/company/etl-project

  • Edit notebooks or scripts
  • Commit and push changes from UI or CLI

🔄 4. Branching Strategy​

Meera recommends:

  • main → Production-ready code
  • dev → Development branch
  • feature/* → Experimental features

Developers:

  • Make changes in feature branches
  • Merge to dev after review
  • Promote to main via CI/CD

🛠️ 5. CI/CD for Databricks​

Databricks integrates with standard CI/CD tools:

  • GitHub Actions
  • Azure DevOps Pipelines
  • GitLab CI

Example Workflow:​

  1. Developer commits notebook changes to dev branch
  2. CI pipeline runs:
    • Unit tests on notebooks/scripts
    • Data validation checks
    • Build artifact creation
  3. Merge approved changes to main
  4. CD pipeline deploys notebooks/jobs to production workspace

🧪 6. Example: GitHub Actions for Notebook Deployment​

name: Deploy to Databricks
on:
  push:
    branches: [ main ]
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v3
      - name: Install Databricks CLI
        run: pip install databricks-cli
      - name: Configure Databricks CLI
        run: databricks configure --token
      - name: Deploy Notebooks
        run: databricks workspace import_dir ./notebooks /Workspace/ETL --overwrite
  • Automatically updates production workspace
  • Reduces human errors
  • Ensures reproducibility

🔐 7. Best Practices for Repos & CI/CD​

  1. Use feature branches for development
  2. Review notebooks via pull requests
  3. Test notebooks with unit tests or PyTest
  4. Promote to production via automated pipelines
  5. Tag releases for auditability
  6. Store secrets securely using Databricks secrets
  7. Enable logging and monitoring for CI/CD jobs

📊 8. Real-World Story — Meera’s Team​

Before Repos & CI/CD:

  • Broken pipelines
  • Conflicting notebook versions
  • Manual deployments

After implementing:

  • All notebooks under Git
  • Feature branches tested automatically
  • Production pipelines deployed via CI/CD
  • Team productivity and reliability improved drastically

Meera proudly notes:

“Code promotion and versioning finally work seamlessly.”

📘 Summary​

Databricks Repos + CI/CD enable:

  • ✔ Git-backed version control

  • ✔ Collaborative development

  • ✔ Branching & feature management

  • ✔ Automated testing and deployment

  • ✔ Production reliability

  • ✔ Governance and auditability

A must-have for enterprise-scale Databricks workflows.

👉 Next Topic

Databricks Monitoring Dashboard — Usage, Cost & Metrics