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

Reference Data Use in the Parser Transformation

Reference Data Use in the Parser Transformation

Informatica Developer installs with multiple reference data objects that you can use with the Parser transformation. You can also create reference data objects in the Developer tool.
When you add a reference data object to a Parser transformation, the transformation writes the strings that match a value in the object to new columns that you specify.
The following table describes the types of reference data you can use:
Reference Data Type
Description
Pattern sets
Identifies data values based on the relative position of each value in the string.
Probabilistic models
Adds fuzzy match capabilities to token parsing 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.
Reference tables
Finds strings that match the entries in a database table.
Regular expressions
Identifies strings that match conditions that you define. You can use a regular expression to find a string within a larger string.
Token sets
Identifies strings based on the types of information they contain.
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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