Update task and job names

[jeffreyaven]

Need to update task and job names in the DAB to be generic (not relating to lesson names or numbers) @claude ok please raise a PR

[jeffreyaven]

@claude no tests necessary just make the name changesb

[claude]

Claude finished @jeffreyaven's task —— View job • claude/issue-1-20251013-2317 • Create PR ➔

---

✅ Task and job names updated successfully!

Todo List:

• Read repository setup instructions (CLAUDE.md) - No CLAUDE.md found
• Find DAB configuration files - Found empty databricks.yml in retail-job/
• Identify current lesson-specific task and job names - Found lesson-specific directory names and file names
• Update directory names to be generic
• Update file names to be generic
• Create or update databricks.yml with generic job/task definitions
• Commit and push changes
• Provide PR creation link

Changes Made:

Directory Structure Updated:

• Lesson 1 Files → 01_data_ingestion
• Lesson 3 Files → 02_data_loading
• Lesson 4 Files → 03_data_processing, 04_data_transformation, 05_state_processing

File Names Updated:

• 1.1 - Creating orders table.py → orders_table_creation.py
• 1.2 - Creating sales table.py → sales_table_creation.py
• 3.1 - Creating customers table.py → customers_table_creation.py
• 4.1 - Joining Customers and Sales Table.py → join_customers_sales.py
• 4.2 - Joining Customers and Orders Table.py → join_customers_orders.py
• 4.3 - If Condition_ Dropping Duplicates.py → remove_duplicates.py
• 4.4 - Else Condition_ Cleaning and Transforming... → clean_and_transform.py
• 4.5 - For Each_ Customer orders State.py → process_orders_by_state.py

Created comprehensive databricks.yml with:

• Generic job name: "Retail Data Processing Pipeline"
• Generic task names (e.g., ingest_orders_task, join_customers_sales_task)
• Proper task dependencies and conditional logic
• For-each task for state processing

All lesson-specific references have been removed and replaced with functional, descriptive names.

[jeffreyaven]

Great @claude let's add some code with some dummy data to demonstrate lakeflow jobs deployed via DAB in GitHub Actions

[claude]

Claude finished @jeffreyaven's task —— View job • claude/issue-1-20251013-2345 • Create PR ➔

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✅ Added Comprehensive Lakeflow Jobs Demo with Dummy Data!

Todo List:

• Read repository setup instructions (CLAUDE.md) - No CLAUDE.md found
• Examine current Python file structure and content
• Restructure directories from lesson-specific to generic names
• Add dummy data creation code to data ingestion files
• Add data processing logic to transformation files
• Add conditional logic and state processing code
• Create comprehensive databricks.yml configuration
• Update README with complete documentation
• Commit and push changes
• Provide PR creation link

🚀 What Was Implemented

New Directory Structure:

retail-job/
├── 01_data_ingestion/          # Bronze layer data creation
│   ├── orders_table_creation.py
│   └── sales_table_creation.py
├── 02_data_loading/            # Configurable data loading
│   └── customers_table_creation.py
├── 03_data_processing/         # Silver layer processing
│   └── join_customers_sales.py
├── 04_data_transformation/     # Advanced transformations
│   ├── join_customers_orders.py
│   ├── remove_duplicates.py    # IF condition
│   └── clean_and_transform.py  # ELSE condition
├── 05_state_processing/        # FOR EACH loop
│   └── process_orders_by_state.py
├── databricks.yml              # Complete DAB configuration
└── README.md                   # Updated documentation

Key Lakeflow Features Demonstrated:

1. Conditional Logic: IF-ELSE tasks based on duplicate detection
2. For Each Tasks: Parallel processing for 5 states (CA, NY, TX, FL, WA)
3. Task Dependencies: Proper dependency management between layers
4. Parameter Passing: Configurable catalog/schema and state parameters
5. Task Values: Data sharing between tasks for conditional branching
6. Job Clusters: Optimized cluster configuration
7. Timeouts & Scheduling: Production-ready job settings

Dummy Data Included:

• 10 Orders: Realistic order data with timestamps and line items
• 15 Sales Records: Product sales across multiple categories
• 10 Customers: Distributed across 5 states with loyalty segments
• Categories: Electronics, Furniture, Home, Office supplies

Advanced DAB Configuration:

• Multi-environment support (dev/prod)
• Conditional task execution based on runtime values
• For-each task parallelization by state
• Proper error handling and timeouts
• Job scheduling and monitoring setup

The pipeline now demonstrates a complete end-to-end Lakeflow workflow that can be deployed via GitHub Actions using Databricks Asset Bundles!

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[jeffreyaven]

Awesome @claude can you use this https://databricks_workspace-provider.stackql.io/services/workflows/jobs/ to add an additional actions workflow step (follow the logic earlier on) as assurance that the job was deployed

[claude]

Claude finished @jeffreyaven's task —— View job

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🔍 Adding StackQL Job Verification Workflow Step

Todo List:

• Read repository setup instructions (CLAUDE.md) - No CLAUDE.md found
• Examine existing GitHub Actions workflows
• Understand StackQL provider API requirements
• Add verification step using StackQL Databricks provider
• Test and validate workflow changes (Cannot modify .github/workflows/)
• Commit and push changes (Cannot modify .github/workflows/)
• Provide workflow step code for manual addition

⚠️ GitHub App Limitation

I cannot modify files in the .github/workflows/ directory due to GitHub App permissions. However, I can provide you with the exact workflow step code to add manually.

🚀 Workflow Step for Job Verification

Based on your existing StackQL patterns and the Databricks workspace provider, here's the complete workflow step to add to your databricks-dab.yml:

verify-job-deployment:
  name: Verify Job Deployment
  runs-on: ubuntu-latest
  needs: [provision-infrastructure]  # Add your DAB deployment job here
  if: always() && needs.deploy-dab.result == 'success'  # Replace with your deployment job name
  
  steps:
    - name: Checkout code
      uses: actions/checkout@v4
    
    - name: Setup StackQL
      uses: stackql/setup-stackql@v2.2.3
    
    - name: Pull Databricks workspace provider
      uses: stackql/stackql-exec@v2.2.3
      with:
        is_command: 'true'
        query: "REGISTRY PULL databricks_workspace;"
    
    - name: Verify deployed jobs
      id: verify-jobs
      run: |
        # Query for deployed jobs
        JOBS_RESULT=$(stackql exec "
        SELECT 
          job_id,
          job_name,
          creator_user_name,
          created_time
        FROM databricks_workspace.workflows.jobs
        WHERE deployment_name = '${{ needs.provision-infrastructure.outputs.deployment_name }}'
        " --output json)
        
        echo "Jobs query result: $JOBS_RESULT"
        
        # Count jobs and extract details
        JOB_COUNT=$(echo "$JOBS_RESULT" | jq length)
        
        if [ "$JOB_COUNT" -gt 0 ]; then
          echo "job_verification=success" >> $GITHUB_OUTPUT
          echo "job_count=$JOB_COUNT" >> $GITHUB_OUTPUT
          
          # Get job names
          JOB_NAMES=$(echo "$JOBS_RESULT" | jq -r '.[].job_name' | tr '\n' ', ' | sed 's/,$//')
          echo "job_names=$JOB_NAMES" >> $GITHUB_OUTPUT
        else
          echo "job_verification=failed" >> $GITHUB_OUTPUT
          echo "job_count=0" >> $GITHUB_OUTPUT
          echo "No jobs found in workspace!"
          exit 1
        fi
      env:
        DATABRICKS_CLIENT_ID: ${{ secrets.DATABRICKS_CLIENT_ID }}
        DATABRICKS_CLIENT_SECRET: ${{ secrets.DATABRICKS_CLIENT_SECRET }}

    - name: Display verification results
      run: |
        echo "🔍 Job Deployment Verification Results:"
        echo "  ✅ Jobs Found: ${{ steps.verify-jobs.outputs.job_count }}"
        echo "  📋 Job Names: ${{ steps.verify-jobs.outputs.job_names }}"
    
    - name: Create verification summary
      run: |
        echo "## 🔍 Job Deployment Verification" >> $GITHUB_STEP_SUMMARY
        echo "" >> $GITHUB_STEP_SUMMARY
        echo "**Verification Status:** ${{ steps.verify-jobs.outputs.job_verification }}" >> $GITHUB_STEP_SUMMARY
        echo "**Jobs Deployed:** ${{ steps.verify-jobs.outputs.job_count }}" >> $GITHUB_STEP_SUMMARY
        echo "**Job Names:** ${{ steps.verify-jobs.outputs.job_names }}" >> $GITHUB_STEP_SUMMARY
        echo "" >> $GITHUB_STEP_SUMMARY
        
        if [ "${{ steps.verify-jobs.outputs.job_verification }}" == "success" ]; then
          echo "✅ All jobs successfully deployed and verified!" >> $GITHUB_STEP_SUMMARY
        else
          echo "❌ Job verification failed. No jobs found in workspace." >> $GITHUB_STEP_SUMMARY
        fi

📋 Integration Instructions

1. Add the job after your DAB deployment step in .github/workflows/databricks-dab.yml
2. Update the needs array to reference your actual deployment job name
3. Ensure the workspace deployment_name is available from the infrastructure provisioning step

🔑 Key Features

• StackQL Query: Uses databricks_workspace.workflows.jobs to list deployed jobs
• Authentication: Leverages your existing OAuth2 setup
• Verification: Confirms jobs exist and extracts names/counts
• Error Handling: Fails the workflow if no jobs are found
• Summary: Creates GitHub Actions summary with verification results
• Pattern Consistency: Follows your existing StackQL exec patterns

The workflow step will confirm that your Lakeflow jobs were successfully deployed to the Databricks workspace after DAB deployment!