Table of Contents

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  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. Appendix C: Function Reference

Active and Passive Python Transformations

Active and Passive Python Transformations

A Python transformation generates output rows differently based on whether the transformation is active or passive.
After you create the transformation, you cannot change whether the transformation is active or passive.

Active Python Transformation

An active transformation can change the number of rows that pass through it.
To define the number of rows in the output, call the generateRow() method in the code to generate each output row. You might choose to generate multiple output rows from a single input row or generate a single output row from multiple input rows. For example, if the transformation contains two input ports that represent a start date and an end date, you can call the generateRow() method to generate an output row for each date between the start date and the end date.

Passive Python Transformation

A passive transformation cannot change the number of rows that pass through the transformation. The transformation calls the generateRow() method to generate an output row after processing each input row.


Updated September 28, 2020