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

Multiple-Occurring Fields

Multiple-Occurring Fields

When a field repeats multiple-times in the source data, you can define the field as a multiple-occurring field in the input row hierarchy. The Normalizer transformation can return a separate row for each occurrence of a multiple-occurring field or group of fields in a source.
A source row might contain four quarters of sales by store:
Store
Sales(1)
Sales(2)
Sales(3)
Sales(4)
Store1
100
300
500
700
Store2
250
450
650
850
When you define the Normalizer input hierarchy, you can you combine the four Sales fields into one multiple-occurring field. Define a field name such as Qtr_Sales and configure it to occur four times in the source.
When the output group contains the store data and the sales data, the Normalizer transformation returns a row for each Store and Qtr_Sales combination. The output row contains an index that identifies which instance of Qtr_Sales that is in the output row.
The transformation returns the following rows:
Store
Qtr_Sales
Qtr (GCID)
Store1
100
1
Store1
300
2
Store1
500
3
Store1
700
4
Store2
250
1
Store2
450
2
Store2
650
3
Store2
850
4
When an output group contains single-occurring columns and a multiple-occurring column, the Normalizer returns duplicate data for the single-occurring columns in each output row. For example, Store1 and Store2 repeat for each instance of Qtr_Sales.
A source row might contain more than one level of multiple-occurring data. You can configure the Normalizer transformation to return separate rows at each level based on how you define the input hierarchy.

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