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

Storage Tables

Storage Tables

The Data Masking transformation maintains storage tables for repeatable substitution between sessions. A storage table row contains the source column and a masked value pair. Each time the Data Masking transformation masks a value with a repeatable substitute value, it searches the storage table by dictionary name, locale, column name, input value, and seed. If it finds a row, it returns the masked value from the storage table. If the Data Masking transformation does not find a row, it retrieves a row from the dictionary with a hash key.
The dictionary name format in the storage table is different for a flat file dictionary and a relational dictionary. A flat file dictionary name is identified by the file name. The relational dictionary name has the following syntax:
<Connection object>_<dictionary table name>
Informatica provides scripts that you can run to create a relational storage table. The scripts are in the following location:
<PowerCenter Client installation directory>\client\bin\Extensions\DataMasking
The directory contains a script for Sybase, Microsoft SQL Server, IBM DB2, and Oracle databases. Each script is named Substitution_<database type>. You can create a table in a different database if you configure the SQL statements and the primary key constraints.
You need to encrypt storage tables for substitution masking when you have unencrypted data in the storage and use the same seed value and dictionary to encrypt the same columns.

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