Table of Contents

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  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

Transformations

Transformations

Python transformation

Python transformation

In advanced mode, you can use the Python transformation to define transformation functionality using the Python programming language. The Python transformation can be an active or passive transformation.
You can use the Python transformation to define simple or complex transformation functionality. You can also use the Python transformation to implement machine learning. For example, you can load a pre-trained model through a resource file and use the model to classify input data or to create predictions.
To create a Python transformation, you write the following types of Python code snippets:
  • Pre-partition Python code that runs one time before it processes any input rows.
  • Main Python code that runs when the transformation receives an input row.
  • Post-partition Python code that runs after the transformation processes all input rows.
To use the Python transformation, your organization must have the appropriate licenses.
You can't use the Python transformation with a Graviton-enabled cluster. For more information about Graviton-enabled clusters, see the Administrator help.
When you create a Python transformation, ensure that you review the Python code to verify that it is free from potentially unsafe active content such as queries, remote scripts, or data connections before you run the code in a mapping task.

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