CI/CD Workflows Skill

Overview

Comprehensive CI/CD patterns for Databricks using GitHub Actions, including automated testing, quality gates, multi-environment deployments, and rollback strategies.

Key Benefits:

Automated testing and validation
Consistent deployments
Environment promotion workflows
Quality gates enforcement
Rollback capabilities
Audit trails

When to Use This Skill

Use CI/CD workflows when you need to:

Automate deployment processes
Enforce quality standards
Deploy across multiple environments
Implement approval gates
Track deployment history
Enable rapid iterations
Reduce manual errors

Core Concepts

CI Pipeline

Continuous Integration Workflow:

.github/workflows/ci.yml

on: push: branches: [develop, main] pull_request: branches: [develop, main]

jobs: lint: name: Code Quality Checks runs-on: ubuntu-latest steps: - uses: actions/checkout@v3

  - name: Set up Python
    uses: actions/setup-python@v4
    with:
      python-version: '3.10'

  - name: Install dependencies
    run: |
      pip install black ruff mypy
      pip install -r requirements.txt

  - name: Run Black
    run: black --check src/

  - name: Run Ruff
    run: ruff check src/

  - name: Run MyPy
    run: mypy src/

test: name: Unit Tests runs-on: ubuntu-latest steps: - uses: actions/checkout@v3

  - name: Set up Python
    uses: actions/setup-python@v4
    with:
      python-version: '3.10'

  - name: Install dependencies
    run: |
      pip install pytest pytest-cov pytest-spark
      pip install -r requirements.txt

  - name: Run Tests
    run: pytest tests/ -v --cov=src --cov-report=xml

  - name: Upload Coverage
    uses: codecov/codecov-action@v3
    with:
      file: ./coverage.xml
      fail_ci_if_error: true

validate-bundle: name: Validate Databricks Bundle runs-on: ubuntu-latest steps: - uses: actions/checkout@v3

  - name: Install Databricks CLI
    run: |
      curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh

  - name: Validate Bundle
    env:
      DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
    run: databricks bundle validate -t dev

2. CD Pipeline

Continuous Deployment Workflow:

.github/workflows/cd.yml

on: push: branches: [main] workflow_dispatch: inputs: environment: description: 'Target environment' required: true type: choice options: - dev - staging - prod

jobs: deploy-dev: name: Deploy to Development if: github.event_name == 'push' || github.event.inputs.environment == 'dev' runs-on: ubuntu-latest environment: development steps: - uses: actions/checkout@v3

  - name: Install Databricks CLI
    run: |
      curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh

  - name: Deploy Bundle
    env:
      DATABRICKS_HOST: ${{ secrets.DEV_DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.DEV_DATABRICKS_TOKEN }}
    run: |
      databricks bundle deploy -t dev
      databricks bundle run -t dev integration_tests

deploy-staging: name: Deploy to Staging needs: deploy-dev if: github.event_name == 'push' || github.event.inputs.environment == 'staging' runs-on: ubuntu-latest environment: staging steps: - uses: actions/checkout@v3

  - name: Install Databricks CLI
    run: |
      curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh

  - name: Deploy Bundle
    env:
      DATABRICKS_HOST: ${{ secrets.STAGING_DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.STAGING_DATABRICKS_TOKEN }}
    run: databricks bundle deploy -t staging

  - name: Run Smoke Tests
    env:
      DATABRICKS_HOST: ${{ secrets.STAGING_DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.STAGING_DATABRICKS_TOKEN }}
    run: databricks bundle run -t staging smoke_tests

deploy-prod: name: Deploy to Production needs: deploy-staging if: github.event_name == 'push' || github.event.inputs.environment == 'prod' runs-on: ubuntu-latest environment: name: production url: https://prod-workspace.databricks.com steps: - uses: actions/checkout@v3

  - name: Install Databricks CLI
    run: |
      curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh

  - name: Create Backup
    env:
      DATABRICKS_HOST: ${{ secrets.PROD_DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.PROD_DATABRICKS_TOKEN }}
    run: |
      # Backup current state
      databricks bundle generate deployment-state --target prod > backup-state.json

  - name: Deploy Bundle
    env:
      DATABRICKS_HOST: ${{ secrets.PROD_DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.PROD_DATABRICKS_TOKEN }}
    run: databricks bundle deploy -t prod

  - name: Run Health Checks
    env:
      DATABRICKS_HOST: ${{ secrets.PROD_DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.PROD_DATABRICKS_TOKEN }}
    run: databricks bundle run -t prod health_check

  - name: Notify Deployment
    if: success()
    run: |
      echo "Production deployment successful"
      # Send notification (Slack, email, etc.)

3. Quality Gates

Enforce Quality Standards:

.github/workflows/quality-gates.yml

on: pull_request: branches: [main, develop]

jobs: security-scan: name: Security Vulnerability Scan runs-on: ubuntu-latest steps: - uses: actions/checkout@v3

  - name: Run Bandit Security Scan
    run: |
      pip install bandit
      bandit -r src/ -f json -o bandit-report.json

  - name: Run Safety Check
    run: |
      pip install safety
      safety check --json

code-coverage: name: Code Coverage Gate runs-on: ubuntu-latest steps: - uses: actions/checkout@v3

  - name: Set up Python
    uses: actions/setup-python@v4
    with:
      python-version: '3.10'

  - name: Run Tests with Coverage
    run: |
      pip install pytest pytest-cov
      pip install -r requirements.txt
      pytest tests/ --cov=src --cov-report=term --cov-fail-under=80

data-quality: name: Data Quality Checks runs-on: ubuntu-latest steps: - uses: actions/checkout@v3

  - name: Validate Data Contracts
    run: |
      python scripts/validate_contracts.py

  - name: Check DLT Expectations
    run: |
      python scripts/validate_dlt_expectations.py

4. Rollback Strategy

Automated Rollback:

.github/workflows/rollback.yml

on: workflow_dispatch: inputs: environment: description: 'Environment to rollback' required: true type: choice options: - dev - staging - prod version: description: 'Version to rollback to (commit SHA or tag)' required: true type: string

jobs: rollback: name: Rollback to Previous Version runs-on: ubuntu-latest environment: ${{ github.event.inputs.environment }} steps: - uses: actions/checkout@v3 with: ref: ${{ github.event.inputs.version }}

  - name: Install Databricks CLI
    run: |
      curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh

  - name: Validate Rollback Version
    run: |
      echo "Rolling back to version: ${{ github.event.inputs.version }}"
      databricks bundle validate -t ${{ github.event.inputs.environment }}

  - name: Deploy Previous Version
    env:
      DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
    run: |
      databricks bundle deploy -t ${{ github.event.inputs.environment }}

  - name: Verify Rollback
    env:
      DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
    run: |
      databricks bundle run -t ${{ github.event.inputs.environment }} health_check

Implementation Patterns

Pattern 1: Matrix Testing

Test Across Multiple Versions:

on: [push, pull_request]

jobs: test: runs-on: ubuntu-latest strategy: matrix: python-version: ['3.9', '3.10', '3.11'] spark-version: ['3.3.0', '3.4.0']

steps:
  - uses: actions/checkout@v3

  - name: Set up Python ${{ matrix.python-version }}
    uses: actions/setup-python@v4
    with:
      python-version: ${{ matrix.python-version }}

  - name: Install PySpark ${{ matrix.spark-version }}
    run: |
      pip install pyspark==${{ matrix.spark-version }}
      pip install -r requirements.txt

  - name: Run Tests
    run: pytest tests/ -v

Pattern 2: Blue-Green Deployment

Zero-Downtime Deployments:

on: workflow_dispatch: inputs: color: description: 'Deployment color' required: true type: choice options: - blue - green

jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3

  - name: Deploy to ${{ github.event.inputs.color }}
    env:
      DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
      COLOR: ${{ github.event.inputs.color }}
    run: |
      # Deploy to color-specific namespace
      databricks bundle deploy -t prod --var environment_color=$COLOR

  - name: Run Health Checks
    run: |
      # Verify new deployment
      python scripts/health_check.py --color $COLOR

  - name: Switch Traffic
    if: success()
    run: |
      # Update routing to new color
      python scripts/switch_traffic.py --to $COLOR

  - name: Cleanup Old Deployment
    run: |
      # Remove old color after successful switch
      OLD_COLOR=$([ "$COLOR" == "blue" ] &#x26;&#x26; echo "green" || echo "blue")
      databricks bundle destroy -t prod --var environment_color=$OLD_COLOR --auto-approve

Pattern 3: Canary Deployment

Gradual Rollout:

on: workflow_dispatch: inputs: canary_percentage: description: 'Percentage of traffic for canary' required: true type: choice options: - '10' - '25' - '50' - '100'

jobs: canary-deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3

  - name: Deploy Canary
    env:
      DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
    run: |
      databricks bundle deploy -t prod-canary

  - name: Route Traffic to Canary
    run: |
      python scripts/route_traffic.py \\
        --canary-percentage ${{ github.event.inputs.canary_percentage }}

  - name: Monitor Canary
    timeout-minutes: 30
    run: |
      python scripts/monitor_canary.py \\
        --duration 30 \\
        --error-threshold 0.01

  - name: Promote or Rollback
    run: |
      if python scripts/check_canary_health.py; then
        echo "Canary healthy - promoting"
        databricks bundle deploy -t prod
      else
        echo "Canary unhealthy - rolling back"
        python scripts/rollback_canary.py
        exit 1
      fi

Pattern 4: Scheduled Deployments

Time-Based Deployment Windows:

on: schedule: # Deploy every Tuesday at 2 AM UTC - cron: '0 2 * * 2' workflow_dispatch:

jobs: check-deployment-window: runs-on: ubuntu-latest outputs: should_deploy: ${{ steps.check.outputs.should_deploy }} steps: - name: Check if in deployment window id: check run: | # Check if current time is within deployment window HOUR=$(date +%H) DAY=$(date +%u) if [ "$DAY" == "2" ] && [ "$HOUR" -ge "2" ] && [ "$HOUR" -le "4" ]; then echo "should_deploy=true" >> $GITHUB_OUTPUT else echo "should_deploy=false" >> $GITHUB_OUTPUT fi

deploy: needs: check-deployment-window if: needs.check-deployment-window.outputs.should_deploy == 'true' runs-on: ubuntu-latest environment: production steps: - uses: actions/checkout@v3

  - name: Install Databricks CLI
    run: |
      curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh

  - name: Deploy to Production
    env:
      DATABRICKS_HOST: ${{ secrets.PROD_DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.PROD_DATABRICKS_TOKEN }}
    run: |
      databricks bundle deploy -t prod

  - name: Send Notification
    if: always()
    run: |
      python scripts/send_notification.py \\
        --status ${{ job.status }} \\
        --environment production

Pattern 5: Integration Testing

Automated Integration Tests:

on: push: branches: [develop] schedule: - cron: '0 */4 * * *' # Every 4 hours

jobs: integration-test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3

  - name: Set up Python
    uses: actions/setup-python@v4
    with:
      python-version: '3.10'

  - name: Install dependencies
    run: |
      pip install pytest databricks-connect databricks-sdk
      pip install -r requirements.txt

  - name: Run Integration Tests
    env:
      DATABRICKS_HOST: ${{ secrets.TEST_DATABRICKS_HOST }}
      DATABRICKS_TOKEN: ${{ secrets.TEST_DATABRICKS_TOKEN }}
    run: |
      pytest tests/integration/ -v \\
        --junit-xml=test-results.xml

  - name: Publish Test Results
    if: always()
    uses: EnricoMi/publish-unit-test-result-action@v2
    with:
      files: test-results.xml

  - name: Cleanup Test Data
    if: always()
    run: python scripts/cleanup_test_data.py

Best Practices

Secrets Management

Use GitHub Secrets for credentials

env: DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }} DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}

Never commit secrets to repository

Use environment-specific secrets

Approval Gates

Require manual approval for production

environment: name: production url: https://prod-workspace.databricks.com

Configure required reviewers in GitHub settings

Deployment Notifications

name: Notify Slack if: always() uses: 8398a7/action-slack@v3 with: status: ${{ job.status }} text: 'Deployment to ${{ github.event.inputs.environment }}' webhook_url: ${{ secrets.SLACK_WEBHOOK }}

Artifact Management

name: Upload Bundle Artifact uses: actions/upload-artifact@v3 with: name: databricks-bundle path: | databricks.yml resources/ src/ retention-days: 90

Common Pitfalls to Avoid

Don't:

Skip testing in CI pipeline
Deploy without validation
Hard-code secrets
Deploy directly to production
Skip rollback planning

Do:

Test every change
Validate before deploying
Use secret management
Use staging environments
Plan rollback strategy

Complete Examples

See /examples/ directory for:

full_cicd_pipeline/ : Complete CI/CD setup
blue_green_deployment/ : Zero-downtime deployment

Related Skills

databricks-asset-bundles : Bundle deployment
testing-patterns : Automated testing
data-quality : Quality validation
delta-live-tables : Pipeline deployment

References

GitHub Actions Docs
Databricks CI/CD Best Practices
Databricks CLI

cicd-workflows

Safety Notice

Copy this and send it to your AI assistant to learn

.github/workflows/ci.yml

.github/workflows/cd.yml

.github/workflows/quality-gates.yml

.github/workflows/rollback.yml

Use GitHub Secrets for credentials

Never commit secrets to repository

Use environment-specific secrets

Require manual approval for production

Configure required reviewers in GitHub settings

Source Transparency

Related Skills

compliance-automation

databricks-asset-bundles

medallion-architecture

delta-live-tables