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

Data Processor Transformation in a Non-native Environment

Data Processor Transformation in a Non-native Environment

The Data Processor transformation processing in a non-native environment depends on the engine that runs the transformation.
Consider the support for the following non-native run-time engines:
  • Blaze engine. Supported without restrictions.
  • Spark engine. Supported with restrictions in batch mappings. Not supported in streaming mappings.*
  • Databricks Spark engine. Not supported.
* For information about the Data Processor transformation support on the Spark engine, see the KB article.


Updated November 10, 2020