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. Normalizer Transformation
  33. Merge 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. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Multiple-Occurring Records

Multiple-Occurring Records

You can define multiple-occurring records in the Normalizer transformation source data. Records are groups of fields. Define records in the Normalizer transformation when you need to define groups of source fields that are multiple-occurring.

Multiple-Occurring Records Example

The following Customer row contains customer information with home address information and business address information:
CustomerID FirstName LastName Home_Street Home_City Home_State Home_Country Business_Street Business_City Business_State Business_Country
When you configure the Normalizer transformation, you can define an input structure that contains the customer fields and a multiple-occurring address record. The address record occurs twice. When you configure the Normalizer transformation output groups, you can return the Address record to a different target than the CustomerID, FirstName, and LastName fields.
The following example shows an input structure with a multiple-occurring address record:
CustomerID FirstName LastName Address (occurs twice) Street City State Country
Subrecords are records within records. When you define records and subrecords, you define an input hierarchy of fields in the source row. Each record is a node in a hierarchy that you can reference when you define the transformation output.
For example, the source row might contain multiple phone numbers for each address type:
CustomerID FirstName LastName Home_Street Home_City Home_State Home_Country Telephone_No Cell_Phone_No Alternate_Phone_No Business_Street Business_City Business_State Business_Country Business_Telephone_No Business_Cell_Phone_No Business-Alternate_Phone1
You define an input hierarchy where Address is the parent of Phone. When you define the Normalizer transformation output, you can return the addresses and the phone numbers to separate targets than the customer information.
Define an input hierarchy similar to the following example:
CustomerID FirstName LastName Address (occurs twice) Street City State Country Phone Telephone_No (occurs three times)

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