Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Aggregator transformation
  6. Cleanse transformation
  7. Data Masking transformation
  8. Data Services transformation
  9. Deduplicate transformation
  10. Expression transformation
  11. Filter transformation
  12. Hierarchy Builder transformation
  13. Hierarchy Parser transformation
  14. Hierarchy Processor transformation
  15. Input transformation
  16. Java transformation
  17. Java transformation API reference
  18. Joiner transformation
  19. Labeler transformation
  20. Lookup transformation
  21. Machine Learning transformation
  22. Mapplet transformation
  23. Normalizer transformation
  24. Output transformation
  25. Parse transformation
  26. Python transformation
  27. Rank transformation
  28. Router transformation
  29. Rule Specification transformation
  30. Sequence Generator transformation
  31. Sorter transformation
  32. SQL transformation
  33. Structure Parser transformation
  34. Transaction Control transformation
  35. Union transformation
  36. Velocity transformation
  37. Verifier transformation
  38. 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.

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