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  • Informatica Data Quality 10.4.0
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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. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Normalizer Transformation
  33. Merge Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

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Key Generator Transformation Configuration

Key Generator Transformation Configuration

When you configure the Key Generator transformation, connect the data source ports that you want to analyze. Specify the port that contains the group key data. If the records do not include a unique identifier, use the sequence ID port to add unique identifiers to the record.
When you specify the group key port, consider the following guidelines:
  • Select a port that contains values that repeat regularly in the port data. Preferably. select a port that creates groups that are similar in size.
  • Select a port that is not relevant to the duplicate analysis.
In the current example, select the City port as the group key. If an account name appears more than once in a city, the accounts might contain duplicate data. If an account name appears more than once in different cities, the accounts are unlikely to be duplicates.
Tip:
 Run a column profile on the data source before you select the group key port. The profile results can indicate the number of times each value appears on a port.
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