Table of Contents

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

Transformations

Transformations

Advanced properties

Advanced properties

You can configure advanced properties for a Router transformation. The advanced properties control settings such as the tracing level for session log messages and whether the transformation is optional or required.
The properties that are available vary based on the mapping mode.
You can configure the following properties:
Property
Description
Tracing Level
Detail level of error and status messages that
Data Integration
writes in the session log. You can choose terse, normal, verbose initialization, or verbose data. Default is normal.
Optional
Determines whether the transformation is optional. If a transformation is optional and there are no incoming fields, the
mapping
task can run and the data can go through another branch in the data flow. If a transformation is required and there are no incoming fields, the task fails.
For example, you configure a parameter for the source connection. In one branch of the data flow, you add a transformation with a field rule so that only Date/Time data enters the transformation, and you specify that the transformation is optional. When you configure the
mapping
task, you select a source that does not have Date/Time data. The
mapping
task ignores the branch with the optional transformation, and the data flow continues through another branch of the mapping.

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