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. Merge Transformation
  33. Normalizer 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. Write Transformation
  52. Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Preface

Preface

The
Informatica Developer Transformation Guide
contains information about transformation functionality in the Developer tool. It is written for data quality, big data, and data services developers. This guide assumes that you have an understanding of data quality concepts, flat file and relational database concepts, and the database engines in your environment.
Each of the run-time engines in the non-native environment can process mapping logic differently. In the non-native environment, Informatica transformations might be fully supported, supported with restrictions, or not supported. Similarly, in the native environment, some Informatica transformations and transformation behavior might not be supported.
Before you validate and run a mapping in the non-native environment, refer to the
Big Data Management User Guide
to learn about the transformations that are supported in the non-native environment and the processing restrictions.

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