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. Deduplicate transformation
  9. Expression transformation
  10. Filter transformation
  11. Hierarchy Builder transformation
  12. Hierarchy Parser transformation
  13. Input transformation
  14. Java transformation
  15. Java transformation API reference
  16. Joiner transformation
  17. Lookup transformation
  18. Mapplet transformation
  19. Normalizer transformation
  20. Output transformation
  21. Parse transformation
  22. Rank transformation
  23. Router transformation
  24. Rule Specification transformation
  25. Sequence Generator transformation
  26. Sorter transformation
  27. SQL transformation
  28. Structure Parser transformation
  29. Transaction Control transformation
  30. Union transformation
  31. Verifier transformation
  32. 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.


Updated June 30, 2020