Table of Contents


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

Python Transformation on the Spark Engine

Python Transformation on the Spark Engine

Mapping validation fails if a user-defined default value is assigned to an output port.
The mapping fails in the following situations:
  • An output port is not assigned a value in the Python code.
  • The data types in corresponding input and output ports are not the same, and the Python code does not convert the data type in the input port to the data type in the output port.
  • The Python transformation contains decimal ports and high precision is enabled in the mapping.
The Data Integration Service does not validate Python code.


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