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  • Informatica Data Quality 10.4.1
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Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Macro Transformation
  30. Match Transformation
  31. Match Transformations in Field Analysis
  32. Match Transformations in Identity Analysis
  33. Normalizer Transformation
  34. Merge Transformation
  35. Parser Transformation
  36. Python Transformation
  37. Rank Transformation
  38. Read Transformation
  39. Relational to Hierarchical Transformation
  40. REST Web Service Consumer Transformation
  41. Router Transformation
  42. Sequence Generator Transformation
  43. Sorter Transformation
  44. SQL Transformation
  45. Standardizer Transformation
  46. Union Transformation
  47. Update Strategy Transformation
  48. Web Service Consumer Transformation
  49. Parsing Web Service SOAP Messages
  50. Generating Web Service SOAP Messages
  51. Weighted Average Transformation
  52. Window Transformation
  53. Write Transformation
  54. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Back Next

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.
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