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

Active and passive Python transformations

Active and passive Python transformations

When you create a Python transformation, you specify how it generates output rows by defining the behavior as active or passive. You define the behavior on the
Advanced
tab. By default, the Python transformation is active.
A Python transformation handles output rows based on the behavior as follows:
  • An active transformation can change the number of rows that pass through it.
    To define the number of rows in the output, call the generateRow() method in the code to generate each output row. You might choose to generate multiple output rows from a single input row or generate a single output row from multiple input rows. For example, if the transformation contains two incoming fields that represent a start date and an end date, you can call the generateRow() method to generate an output row for each date between the start date and the end date.
  • A passive transformation cannot change the number of rows that pass through the transformation. The transformation calls the generateRow() method to generate an output row after processing each input row.

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