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

Resource files

Resource files

The Python transformation uses resource files and the Python code to define the transformation functionality. If you use a pre-trained model, you specify the pre-trained model as a resource file in the Python transformation.
The Python transformation contains the following components:
Resource file
A file that contains the resources that you access in the Python code.
The file can be a pre-trained model that has been trained on a larger data set outside
Data Integration
. You can use the pre-trained model to classify data or make predictions based on the data that you pass to the Python transformation. You can access the pre-trained model in the Python code.
Runtime environment
Add resource files based on the type of runtime environment. If you reference resource files in the Python code, add the resource files to the same directory. To maintain consistency, you can store the resource files in a dedicated folder named python_resources.
Consider the following guidelines:
  • If the Secure Agent machine stops unexpectedly and the agent restarts on a different machine, you must add the Python installation and resource files to the same directory on the new machine.
  • If you update the Python installation or resource files on the Secure Agent machine, the files take effect the next time that you run a job in advanced mode.
  • To prevent long-running jobs from failing, do not update the files on the Secure Agent machine more than four times while you have jobs running.
Serverless runtime environment
Add resource files in the supplementary file location.
If you update the Python installation or resource files, you must redeploy the serverless runtime environment for the changes to take effect.
For more information about the supplementary file location, see
Administrator
in the Administrator help.
Python code
The Python code that the Python transformation uses to process data that you pass to the transformation. When you write Python code, you might reconstruct input variables, load a pre-trained model, or define output variables.

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