Table of Contents


  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 Engineering Integration Engines

Data Engineering Integration Engines

When you run a mapping, you can choose to run the mapping in the native environment or in a non-native environment, such as Hadoop or Databricks. When you validate a mapping, you can validate it against one or all of the engines. The Developer tool returns validation messages for each engine.
When you run a mapping in the native environment, the Data Integration Service in the Informatica domain runs the mapping. When you run the mapping in a non-native environment, the Data Integration Service pushes the run-time processing to a compute cluster in the non-native environment.
When you run the mapping in a non-native environment, the Data Integration Service uses a proprietary rule-based methodology to determine the best engine to run the mapping. The rule-based methodology evaluates the mapping sources and the mapping logic to determine the engine. The Data Integration Service translates the mapping logic into code that the engine can process, and it transfers the code to the engine.
The following image shows the processing environments and the run-time engines in the environments:
The diagram shows the Data Integration Service under the Native Environments heading, and the Blaze engine, the Spark engine, and the Databricks Spark engine under the Non-native Environments heading.


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