Table of Contents

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

Transformations

Transformations

Python transformation

Python transformation

In an elastic mapping, 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 cannot use the Python transformation with a Graviton-enabled cluster. For more information on a Graviton-enabled cluster, see
Data Integration Elastic Configuration
.
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.