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Getting Started with Conversor DDF

Getting Started with Conversor DDF

Follow these 5 steps to convert your Tableau Prep flow or DataStage job to validated SQL.

Step 1: Upload Your Flow or Job

Upload your Tableau Prep flow or IBM DataStage job to the system.

  • Click the Upload button on the Workflows page
  • Select your file (maximum 100MB)
    • Tableau Prep: .tfl or .zip files
    • DataStage: .dsx files
  • The system will parse and analyze your file
  • You'll see the workflow details and available outputs

What happens next: The system extracts your workflow structure, identifies all nodes, and prepares conversion options.

Step 2: Create a Conversion Session

Select an output node and initialize the conversion for that specific target.

  • Choose which output node to convert
  • Configure input mappings if needed
    • Excel table references (Tableau Prep)
    • DataStage datasets
  • Review the stage count and execution order
  • Click "Start Session" to begin conversion

What happens next: The system creates a session targeting your selected output and prepares to generate SQL stage-by-stage.

Pro Tip: If your workflow has multiple outputs, create a separate session for each one.

Step 3: Work Through Stages Sequentially

The system guides you through stages, automatically blocking downstream work until upstream dependencies are complete.

For each stage:

  1. Start with the first stage (usually your input)
  2. Generate the SQL for each stage (AI-powered or manual)
  3. Use the 6 validation panels to review your work:
    • Generated SQL
    • Node Configuration
    • Intermediate Query
    • Schema
    • Feedback History
    • LLM Conversation (future)
  4. Approve, edit, or request changes
  5. Move to next stage (blocked stages unlock automatically)

Validation Workflow:

For each stage:
├─ Review Generated SQL
├─ Check Node Configuration
├─ Test Intermediate Query
├─ Verify Schema
├─ Check Feedback History
└─ Approve or Request Changes

Key Concept: Downstream stages remain locked until you approve upstream stages. This ensures data flow consistency.

Step 4: Use Batch Operations (Optional)

Speed up conversion with batch generate and reset operations.

Batch Generate:

  • Generate multiple eligible stages at once
  • Only stages with met dependencies will actually generate
  • Saves time on large workflows

Batch Reset:

  • Reset multiple stages back to pending status
  • Useful if you need to revise multiple stages
  • Dependency rules apply automatically

Pro Tip: Use batch operations after making SQL edits to regenerate dependent stages all at once.

Step 5: Export Your SQL

Once all stages are validated, export your complete SQL script for use in your data warehouse.

  • Review the final stage with all upstream CTEs
  • Export the complete SQL script
  • Use in your data warehouse
  • Version control your SQL alongside your workflows

What you'll get: A single SQL file containing all Common Table Expressions (CTEs) and the final SELECT statement, ready for production use.

Key Concepts

📊 Workflows

Your uploaded Tableau flows or DataStage jobs. Each workflow can have multiple versions that you can update without losing conversion history.

🎯 Sessions

A conversion session targets one specific output node. If your workflow has 3 output nodes, create 3 sessions to generate SQL for each output.

📋 Stages

Each node in your flow becomes a stage. You validate and approve each stage's SQL individually. Stages are processed sequentially with dependency management.

🔗 Dependencies

Downstream stages are automatically blocked until their upstream dependencies are generated and validated. This prevents invalid queries and ensures data consistency.

Pro Tips

💬 Use the Intermediate Query Panel

The intermediate query shows your complete SQL up to the current stage. Test it in your database to validate data flow and catch issues early.

🔄 Use Batch Operations for Efficiency

Generate multiple stages at once, but remember only eligible stages (with dependencies met) will actually generate. Non-eligible stages are automatically skipped.

✏️ Don't Hesitate to Edit the SQL

The AI-generated SQL is a starting point. Edit freely to match your requirements. Your edits are preserved and won't be overwritten on subsequent generations.

📊 Check the Schema Panel for Downstream

The column names in the Schema are critical. Downstream stages will reference these exact names in their transformations. Get them right!

📝 Review Node Configuration

Understanding the original node's configuration helps you validate the generated SQL. It shows inputs, parameters, and settings from your original flow.

Next Steps

Questions? Refer to the Validation Panels Guide for detailed explanations of each review interface.

Updated 1 day ago