Table of Contents


  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings
  4. Sources
  5. Targets
  6. Transformations
  7. Data Preview
  8. Cluster Workflows
  9. Profiles
  10. Monitoring
  11. Hierarchical Data Processing
  12. Hierarchical Data Processing Configuration
  13. Hierarchical Data Processing with Schema Changes
  14. Intelligent Structure Models
  15. Stateful Computing
  16. Appendix A: Connections
  17. Appendix B: Data Type Reference
  18. Appendix C: Function Reference

Aggregator Transformation on the Databricks Spark Engine

Aggregator Transformation on the Databricks Spark Engine

Some processing rules for the Databricks Spark engine differ from the processing rules for the Data Integration Service.

Mapping Validation

Mapping validation fails in the following situations:
  • The transformation contains stateful variable ports.
  • The transformation contains unsupported functions in an expression.

Aggregate Functions

If you use a port in an expression in the Aggregator transformation but you do not use the port within an aggregate function, the run-time engine might use any row in the port to process the expression.
The row that the run-time engine uses might not be the last row in the port. Processing is distributed, and thus the run-time engine might not be able to determine the last row in the port.

Data Cache Optimization

You cannot optimize the data cache for the transformation to store data using variable length.


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