Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings in the Hadoop Environment
  4. Mapping Sources in the Hadoop Environment
  5. Mapping Targets in the Hadoop Environment
  6. Mapping Transformations in the Hadoop Environment
  7. Processing Hierarchical Data on the Spark Engine
  8. Configuring Transformations to Process Hierarchical Data
  9. Processing Unstructured and Semi-structured Data with an Intelligent Structure Model
  10. Stateful Computing on the Spark Engine
  11. Monitoring Mappings in the Hadoop Environment
  12. Mappings in the Native Environment
  13. Profiles
  14. Native Environment Optimization
  15. Cluster Workflows
  16. Connections
  17. Data Type Reference
  18. Function Reference
  19. Parameter Reference

Python Transformation Support on the Spark Engine

Python Transformation Support on the Spark Engine

Mapping validation fails if a user-defined default value is assigned to an output port.
Mapping execution 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 Data Integration Service does not validate Python code.


Updated October 23, 2019