Table of Contents

Search

  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Aggregator transformation
  6. Cleanse transformation
  7. Data Masking transformation
  8. Deduplicate transformation
  9. Expression transformation
  10. Filter transformation
  11. Hierarchy Builder transformation
  12. Hierarchy Parser transformation
  13. Hierarchy Processor transformation
  14. Input transformation
  15. Java transformation
  16. Java transformation API reference
  17. Joiner transformation
  18. Labeler transformation
  19. Lookup transformation
  20. Mapplet transformation
  21. Normalizer transformation
  22. Output transformation
  23. Parse transformation
  24. Python transformation
  25. Rank transformation
  26. Router transformation
  27. Rule Specification transformation
  28. Sequence Generator transformation
  29. Sorter transformation
  30. SQL transformation
  31. Structure Parser transformation
  32. Transaction Control transformation
  33. Union transformation
  34. Velocity transformation
  35. Verifier transformation
  36. Web Services transformation

Transformations

Transformations

Union transformation

Union transformation

The Union transformation is an active transformation that you use to merge data from multiple pipelines into a single pipeline.
For data integration patterns, it is common to combine two or more data sources into a single stream that includes the union of all rows. The data sources often do not have the same structure, so you cannot freely join the data streams. The Union transformation enables you to make the metadata of the streams alike so that you can combine the data sources in a single target.
The Union transformation merges data from multiple sources similar to the UNION ALL SQL statement. For example, you might use the Union transformation to merge employee information from ADP with data from a Workday employee object.
You can add, change, or remove specific fields when you merge data sources with a Union transformation.
At run time, the
mapping
task processes input groups in parallel. It concurrently reads the sources connected to the Union transformation and pushes blocks of data into the input groups of the transformation. As the mapping runs, it merges data into a single output group based on the field mappings.
To use the Union transformation, you need the appropriate license.