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

Masking Techniques

Masking Techniques

The masking technique is the type of data masking to apply to the selected column.
You can select one of the following masking techniques for an input column:
Random
Produces random, non-repeatable results for the same source data and masking rules. You can mask date, numeric, and string datatypes. Random masking does not require a seed value. The results of random masking are non-deterministic.
Expression
Applies an expression to a source column to create or mask data. You can mask all datatypes.
Key
Replaces source data with repeatable values. The Data Masking transformation produces deterministic results for the same source data, masking rules, and seed value. You can mask date, numeric, and string datatypes.
Substitution
Replaces a column of data with similar but unrelated data from a dictionary. You can mask the string datatype.
Dependent
Replaces the values of one source column based on the values of another source column. You can mask the string datatype.
Tokenization
Replaces source data with data generated based on customized masking criteria. The Data Masking transformation applies rules specified in a customized algorithm. You can mask the string datatype.
Special Mask Formats
Credit card number, email address, IP address, phone number, SSN, SIN, or URL. The Data Masking transformation applies built-in rules to intelligently mask these common types of sensitive data.
No Masking
The Data Masking transformation does not change the source data.
Default is No Masking.

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