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

Match Performance in Identity Analysis

Match Performance in Identity Analysis

To improve the mapping performance when you perform identity analysis on two data sets, configure the Match transformation to read identity index data from database tables. Run a mapping to create the index tables for the master data set. Run the mapping again to compare the index data to another data source.
Use the options on the
Match Type
view to identify the database tables that store the index data. Use the same options to select the index tables when you configure the transformation to compare the index data to data from another source.
To write the index data to database tables, perform the following tasks:
  1. Create a mapping that reads a data source of identity information.
  2. Configure a Match transformation in the mapping to write the index data to a database.
  3. Run the mapping to generate the index data. The index data represents a data store that you can reuse.
To read the index data from database tables, perform the following tasks:
  1. Create a mapping that reads another identity data source.
  2. Configure a Match transformation in the mapping to read the index data from the database that you specified earlier.
    When the mapping data source and the index data share a common structure, you can reuse the mapping that generated the index data.
  3. Run the mapping to compare the data source to the index data.
    The mapping generates index data for the data source. The mapping does not need to generate index data for the larger data set. Therefore, the mapping runs faster than a dual-source mapping that generates index data for both data sets.

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