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

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.
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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