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  • Common Content for Data Engineering 10.5.4
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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

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Normalizer Transformation Overview

Normalizer Transformation Overview

The Normalizer transformation is an active transformation that transforms one source row into multiple target rows. When the Normalizer transformation receives a row that contains multiple-occurring data, it returns a row for each instance of the multiple-occurring data.
The Normalizer transformation parses multiple-occurring data into separate output rows. For example, a relational source row might contain four quarters of sales. The Normalizer transformation generates a separate output row for each sales occurrence.
When you define the Normalizer transformation, you configure an input row hierarchy that describes the source data structure. Optionally, you can define records in the transformation input hierarchy. A record is a container for a group of fields. Define a record when a group of fields occurs multiple times in the source data. The input hierarchy determines how you can configure the transformation output groups.
The Normalizer transformation transforms data from relational tables or from flat file sources.
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