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



Comparison to Joiner transformation

Comparison to Joiner transformation

A Union transformation can merge data from multiple sources but does not combine data based on a join condition like a Joiner transformation.
The following table identifies some key differences between the Union transformation and Joiner transformation, which also merges data from multiple sources. Factor these differences into your mapping design:
Union transformation
Joiner transformation
Combine records based on a join condition
No. The Union Transformation is equivalent to a UNION ALL statement in SQL, which combines data vertically from multiple sources.
Yes. The Joiner transformation supports Normal, Right Outer, Left Outer, and Full Outer JOINs.
Include multiple input groups
Yes. You can define multiple input groups and one output group.
Yes. You can define two input groups, Master and Detail.
Include heterogeneous sources
Merge different data types
All of the source columns must have similar data types. The number of columns in each source must be the same.
At least one column in the sources to be joined must have the same data type.
Generate transactions


We’d like to hear from you!
pankaj kumar Sarkar - August 29, 2023

Hi Team,

I am new to IICS , but regarding the comparison on Union VS Joiner - "Include heterogeneous sources" , seems Joiner Transformer supports heterogeneous sources ( Just have joined successfully Flat file(csv) with Salesforce file). Can you please explain more in this points. 

Informatica Documentation Team - August 29, 2023

Hi Pankaj Kumar Sarkar,

Thanks for reaching out! You are correct - the Joiner transformation can join data from two related heterogeneous sources.

We'll file a documentation issue to get this topic clarified by the next major release.


v S - December 29, 2023

How can we remove duplicates using JOINER? Can you please post a detailed explanation?


Informatica Documentation Team - January 06, 2024

Hi v S,

Thanks for reaching out! We've forwarded your question to our QA team and will get back to you shortly.

Informatica Documentation Team - January 12, 2024

Hi again v S,

We've confirmed with our QA team that this seems to be an error in the documentation. You can't remove duplicates using the Joiner transformation. However, you can use a Router or Filter transformation to do this.

We'll update this topic for the next release.

Thanks so much for bringing this to our attention!