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

Input Hierarchy Definition

Input Hierarchy Definition

When you create a Normalizer transformation, you define an input hierarchy that describes records and fields in the source. Define the input hierarchy on the
Normalizer
view of the transformation.
The Developer tool creates the transformation input ports based on the fields that you define in the input hierarchy. Define the input group structure before you define the transformation output groups.
When you define an input hierarchy, you must define an input structure that corresponds to the structure of the source data. The source data might contain more than one group of multiple-occurring fields. To define the structure, you can configure a record that occurs at the same level as another record in the source. Or, you can define records that occur within other records.

Input Hierarchy Example

The following source row contains customer fields and an address record that occurs twice:
CustomerID FirstName LastName Address Street City State Country Address1 Street1 City1 State1 Country1
When you define the input structure in the
Normalizer
view, you can add the CustomerID, FirstName, and LastName as fields. Define an Address record and include the Street, City, State, and Country fields in the address. Change the Address Occurs value to 2.
The following image shows the input hierarchy in the
Normalizer
view:
The example Normalizer view shows the CustomerID, FirstName, and LastName fields. At the same level is an Address record. The Occurs value for Address is 2. Within Address, is Street, City, State, and Country. These fields are indented and the level is 2.
The
Occurs
column in the
Normalizer
view identifies the number of instances of a field or record in a source row. Change the value in the
Occurs
column for multiple-occurring fields or records. In this example, the customer fields occur one time, and the Address record occurs twice.
The
Level
column in the
Normalizer
view indicates where a field or record appears in the input hierarchy. The customer fields are at level 1 in the hierarchy. The Address record is also level 1.

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