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

Expression Transformation on the Databricks Spark Engine

Expression Transformation on the Databricks Spark Engine

Mapping validation fails in the following situations:
  • The transformation contains stateful variable ports.
  • The transformation contains unsupported functions in an expression.
If an expression results in numerical errors, such as division by zero or SQRT of a negative number, it returns a null value and rows do not appear in the output. In the native environment, the expression returns an infinite or an NaN value.


Updated September 24, 2020