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Table of Contents

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  1. Preface
  2. Working with Transformations
  3. Address Validator Transformation
  4. Aggregator Transformation
  5. Association Transformation
  6. Bad Record Exception Transformation
  7. Case Converter Transformation
  8. Classifier Transformation
  9. Cleanse transformation
  10. Comparison Transformation
  11. Custom Transformation
  12. Custom Transformation Functions
  13. Consolidation Transformation
  14. Data Masking Transformation
  15. Data Masking Examples
  16. Decision Transformation
  17. Duplicate Record Exception Transformation
  18. Dynamic Lookup Cache
  19. Expression Transformation
  20. External Procedure Transformation
  21. Filter Transformation
  22. HTTP Transformation
  23. Identity Resolution Transformation
  24. Java Transformation
  25. Java Transformation API Reference
  26. Java Expressions
  27. Java Transformation Example
  28. Joiner Transformation
  29. Key Generator Transformation
  30. Labeler Transformation
  31. Lookup Transformation
  32. Lookup Caches
  33. Match Transformation
  34. Match Transformations in Field Analysis
  35. Match Transformations in Identity Analysis
  36. Merge Transformation
  37. Normalizer Transformation
  38. Parser Transformation
  39. Rank Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. Source Qualifier Transformation
  44. SQL Transformation
  45. Using the SQL Transformation in a Mapping
  46. Stored Procedure Transformation
  47. Standardizer Transformation
  48. Transaction Control Transformation
  49. Union Transformation
  50. Unstructured Data Transformation
  51. Update Strategy Transformation
  52. Weighted Average Transformation
  53. XML Transformations

Transformation Guide

Transformation Guide

Normalizer Transformation Overview

Normalizer Transformation Overview

The Normalizer transformation receives a row that contains multiple-occurring columns and returns a row for each instance of the multiple-occurring data. The transformation processes multiple-occurring columns or multiple-occurring groups of columns in each source row. The Normalizer transformation is an active transformation.
The Normalizer transformation parses multiple-occurring columns from COBOL sources, relational tables, or other sources. It can process multiple record types from a COBOL source that contains a REDEFINES clause.
For example, you might have a relational table that stores four quarters of sales by store. You need to create a row for each sales occurrence. You can configure a Normalizer transformation to return a separate row for each quarter.
The following source rows contain four quarters of sales by store:
Store1 100 300 500 700 Store2 250 450 650 850
The Normalizer returns a row for each store and sales combination. It also returns an index that identifies the quarter number:
Store1 100 1 Store1 300 2 Store1 500 3 Store1 700 4 Store2 250 1 Store2 450 2 Store2 650 3 Store2 850 4
The Normalizer transformation generates a key for each source row. The Integration Service increments the generated key sequence number each time it processes a source row. When the source row contains a multiple-occurring column or a multiple-occurring group of columns, the Normalizer transformation returns a row for each occurrence. Each row contains the same generated key value.
When the Normalizer returns multiple rows from a source row, it returns duplicate data for single-occurring source columns. For example, Store1 and Store2 repeat for each instance of sales.
You can create a VSAM Normalizer transformation or a pipeline Normalizer transformation:
  • VSAM Normalizer transformation.
    A non-reusable transformation that is a Source Qualifier transformation for a COBOL source. The Mapping Designer creates VSAM Normalizer columns from a COBOL source in a mapping. The column attributes are read-only. The VSAM Normalizer receives a multiple-occurring source column through one input port.
  • Pipeline Normalizer transformation.
    A transformation that processes multiple-occurring data from relational tables or flat files. You create the columns manually and edit them in the Transformation Developer or Mapping Designer. The pipeline Normalizer transformation represents multiple-occurring columns with one input port for each source column occurrence.

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