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. Data Services transformation
  9. Deduplicate transformation
  10. Expression transformation
  11. Filter transformation
  12. Hierarchy Builder transformation
  13. Hierarchy Parser transformation
  14. Hierarchy Processor transformation
  15. Input transformation
  16. Java transformation
  17. Java transformation API reference
  18. Joiner transformation
  19. Labeler transformation
  20. Lookup transformation
  21. Machine Learning transformation
  22. Mapplet transformation
  23. Normalizer transformation
  24. Output transformation
  25. Parse transformation
  26. Python transformation
  27. Rank transformation
  28. Router transformation
  29. Rule Specification transformation
  30. Sequence Generator transformation
  31. Sorter transformation
  32. SQL transformation
  33. Structure Parser transformation
  34. Transaction Control transformation
  35. Union transformation
  36. Velocity transformation
  37. Verifier transformation
  38. 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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