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  • Informatica Data Quality 10.4.0
  • All Products

Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Normalizer Transformation
  33. Merge Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

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Python Transformation

Python Transformation

The Python transformation provides an interface to define transformation functionality using Python code.
Python is a language that uses simple syntax, dynamic typing, and dynamic binding, making Python an ideal choice to increase productivity or to participate in rapid application development. When you use your Python code in a data engineering mapping, the Python code is embedded into the generated Scala code that the Spark or Databricks Spark engine runs to process large, diverse, and fast-changing data sets.
You can also use the Python transformation for machine learning. In the transformation, you can specify a resource file that contains a pre-trained model and load the pre-trained model in the Python code. For example, you can load a pre-trained model to classify input data or to create predictions.
Before you can use the Python transformation, configure the corresponding Spark advanced properties in the Hadoop connection or Databricks connection properties. Then, ensure that the worker nodes on the cluster contain an installation of Python.
For more information about installing Python, see the
Data Engineering Integration Guide
.
You can only run the Python transformation on the Spark or Databricks Spark engine. You cannot run the Python transformation in the native environment.
Effective in version 10.4.0, the Python transformation is supported for technical preview on the Databricks Spark engine.
Technical preview functionality is supported for evaluation purposes but is unwarranted and is not production-ready. Informatica recommends that you use in non-production environments only. Informatica intends to include the preview functionality in an upcoming release for production use, but might choose not to in accordance with changing market or technical circumstances. For more information, contact Informatica Global Customer Support.
For more information about the Python transformation, see the
Data Engineering Integration User Guide
.
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