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

Reference Data Use in the Labeler Transformation

Reference Data Use in the Labeler Transformation

Informatica Developer installs with different types of reference data objects that you can use with the Labeler transformation. You can also create reference data objects.
When you add a reference data object to Labeler transformation strategy, the transformation searches the input data on the strategy for values in the reference data object. The transformation replaces any value it finds with a valid value from the reference data object, or with a value that you specify.
The following table describes the types of reference data you can use:
Reference Data Type
Description
Character sets
Identifies different types of characters, such as letters, numbers, and punctuation symbols.
Use in character labeling operations.
Probabilistic models
Adds fuzzy match capabilities to token label operations. The transformation can use a probabilistic model to infer the type of information in a string. To enable the fuzzy match capabilities, you compile the probabilistic model in the Developer tool.
Use in token labeling operations.
Reference tables
Finds strings that match the entries in a database table.
Use in token labeling and character labeling operations.
Regular expressions
Identifies strings that match conditions that you define. You can use a regular expression to find a string within a larger string.
Use in token labeling operations.
Token sets
Identifies strings based on the types of information they contain.
Use in token labeling operations.
Informatica installs with token sets different types of token definitions, such as word, telephone number, post code, and product code definitions.

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