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

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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