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

Lookup Transformation on the Databricks Spark Engine

Lookup 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:
  • Case sensitivity is disabled.
  • The lookup condition contains binary data type.
  • The lookup condition uses a field with a complex data type.
  • The cache is configured to be shared, named, persistent, dynamic, or uncached. The cache must be a static cache.
  • The lookup source is not Microsoft Azure SQL Data Warehouse.
The mapping fails in the following situation:
  • The transformation is unconnected and used with a Joiner transformation.

Multiple Matches

When you choose to return the first, last, or any value on multiple matches, the Lookup transformation returns any value.
If you configure the transformation to report an error on multiple matches, the Spark engine drops the duplicate rows and does not include the rows in the logs.
If an HBase lookup does not result in a match, it generates a row with null values for all columns. You can add a Filter transformation after the Lookup transformation to filter out null rows.


Updated September 24, 2020