Visual Editor for Data Warehouse Transformations

A new visual editor for Data Warehouse Transformations (DWT) introduces a self-serve, low-code capability within Fusion Unity that enables users to design and execute data transformations after ingestion as part of the Data Warehouse (DW) job workflow.

Complementing the existing ingest-time transformations, it allows teams to refine and enrich the data as it moves from raw to Data Warehouse (DW) tables. This separation supports flexible, multi-stage data processing - standardizing data during ingestion and enhancing it within the warehouse based on evolving business needs.

Available under 'Data Workbench' within Fusion Unity, the editor supports joining multiple data objects, applying conditional logic, and defining transformation sequences within a governed framework. Once published, transformations run automatically as part of the DW job.

Ideally used for for preprocessing of complex business logic - such as derived attributes, calculated metrics, or data enrichment - into persistent data objects. Shifting this logic to batch processing improves segmentation performance, scalability, and downstream activation efficiency.

Business Benefits:

  • Low-code, visual transformation interface
  • Moves complex computation to Data Warehouse processing
  • Transforms data quality through centralized logic
  • Enhances scalability by shifting runtime computation to batch processing

Steps to enable and configure

You don't need to do anything to enable this feature.

Tips and considerations

How to Start Using

  • Once you're within Fusion Unity:
    • Navigate to Data Workbench from the Unity navigation menu
    • Click Create > Data Warehouse Transformation
    • Define the transformation name and description
    • Use the visual canvas to:
      • Select source data objects
      • Configure joins and filters
      • Define transformation logic and expressions
      • Map attributes to the destination object
    • Save and Publish the transformation
    • Once published, the transformation runs automatically as part of the Data Warehouse job

Tips and Considerations

  • Use DW transformations for complex or reusable logic to reduce downstream computation
  • Leverage joins and filters to create enriched, analysis-ready datasets
  • Maintain clear naming conventions for governance and reuse
  • Manage execution order when multiple transformations target the same object
  • Use the expression builder for advanced logic (e.g., CASE, DATE functions)
  • Monitor execution via the Jobs dashboard
  • Known Limitations:
    • Maximum of three active transformations per destination data object
    • Up to three joins supported per transformation
    • Joins between the same instance of a data object are not supported

Access requirements

  • Ensure users have appropriate roles:
    • Customer Data Platform Administrator (ORA_FMK_CUSTOMER_DATA_PLATFORM_ADMINISTRATOR)
    • Customer Data Platform Manager (ORA_FMK_CUSTOMER_DATA_PLATFORM_MANAGER)
    • Customer Data Platform Specialist / Analyst