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. 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 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

Transformations

Transformations

Advanced properties

Advanced properties

You can configure advanced properties for a Filter 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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