Table of Contents


  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. B2B transformation
  7. Aggregator transformation
  8. Cleanse transformation
  9. Data Masking transformation
  10. Data Services transformation
  11. Deduplicate transformation
  12. Expression transformation
  13. Filter transformation
  14. Hierarchy Builder transformation
  15. Hierarchy Parser transformation
  16. Hierarchy Processor transformation
  17. Input transformation
  18. Java transformation
  19. Java transformation API reference
  20. Joiner transformation
  21. Labeler transformation
  22. Lookup transformation
  23. Machine Learning transformation
  24. Mapplet transformation
  25. Normalizer transformation
  26. Output transformation
  27. Parse transformation
  28. Python transformation
  29. Rank transformation
  30. Router transformation
  31. Rule Specification transformation
  32. Sequence Generator transformation
  33. Sorter transformation
  34. SQL transformation
  35. Structure Parser transformation
  36. Transaction Control transformation
  37. Union transformation
  38. Velocity transformation
  39. Verifier transformation
  40. Web Services transformation



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
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.


We’d like to hear from you!
Manish Kumar - May 08, 2023


Does Union operator not support addition of new group as i need to add new group as i have more than 2 data source?

Informatica Documentation Team - May 08, 2023

Hi Manish Kumar,

You can use the Union transformation with more than two data sources. To merge data from more than two sources, add an input group for each additional source.

In the Mapping Designer, connect an upstream transformation to the "New Group" group of the Union transformation. You can also add input groups on the Incoming Fields tab.

For more information, please see the "Union transformation > Input groups" topic.