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

Substitution Masking

Substitution Masking

Substitution masking replaces a column of data with similar but unrelated data. Use substitution masking to replace production data with realistic test data. When you configure substitution masking, define the dictionary that contains the substitute values.
The Data Masking transformation performs a lookup on the dictionary that you configure. The Data Masking transformation replaces source data with data from the dictionary. Dictionary files can contain string data, datetime values, integers, and floating point numbers. Enter datetime values in the following format:
mm/dd/yyyy
You can substitute data with repeatable or non-repeatable values. When you choose repeatable values, the Data Masking transformation produces deterministic results for the same source data and seed value. You must configure a seed value to substitute data with deterministic results.The Integration Service maintains a storage table of source and masked values for repeatable masking.
You can substitute more than one column of data with masked values from the same dictionary row. Configure substitution masking for one input column. Configure dependent data masking for the other columns that receive masked data from the same dictionary row.

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