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

Rules and Guidelines for Mappings on the Databricks Spark Engine

Rules and Guidelines for Mappings on the Databricks Spark Engine

A non-native environment like Databricks Spark uses distributed processing and processes data on different nodes. Each node does not have access to the data that is being processed on other nodes. As a result, the runtime engine might not be able to determine the order in which the data originated. So, when you run a mapping in a non-native environment and you run the same mapping in the native environment, both mappings return correct results, but the results might not be identical. For more information, see the .
Consider the following run-time differences on the Databricks Spark engine:
  • When you use the auto optimizer level, the early selection optimization method is enabled if the mapping contains any data source that supports pushing filters to the source on Spark or Databricks Spark engines. For more information about optimizer levels, see the
    Informatica Developer Mapping Guide
  • Databricks Spark performs auto-optimization on jobs based on configuration settings for the cluster. If you set optimization through the Spark.default.parallelism property, the Databricks Spark engine ignores this setting. As a consequence, it is not possible to configure optimization on the job level.


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