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



In this example, you are creating several mappings that ingest and convert customer data.
You create a project with several mappings. Each uses the same relational source and writes to the same target.
As you create one of the mappings, a CLAIRE rule finds that the mapping uses a JDBC connection and is configured to use the Blaze run-time engine. CLAIRE raises the following recommendation, which you view in the
tab of the
Mappings that use a JDBC connection can run faster when you enable Sqoop on the connection and run the mapping on the Spark engine.
You click
Show Me
, and the
view opens to settings where you can implement both recommendations.
As development continues, you notice that recommendations of the same type repeatedly appepar in the
tab of the
view. You know how you will address the issue, but decide to do it at a later time. To stop seeing the same recommendation, you disable the category of recommendation for the project.
When the mappings in the project are complete, you run an analysis on the entire project. CLAIRE analyzes the mappings and finds that their similar structures indicate that some of the mappings could be combined into a single mapping. You use the analysis spreadsheet to spot similarities between the mappings. After you identify the similar mappings, you edit the mappings in the project to eliminate duplicative logic.


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