Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Aggregator transformation
  6. Cleanse transformation
  7. Data Masking transformation
  8. Deduplicate transformation
  9. Expression transformation
  10. Filter transformation
  11. Hierarchy Builder transformation
  12. Hierarchy Parser transformation
  13. Hierarchy Processor transformation
  14. Input transformation
  15. Java transformation
  16. Java transformation API reference
  17. Joiner transformation
  18. Labeler transformation
  19. Lookup transformation
  20. Mapplet transformation
  21. Normalizer transformation
  22. Output transformation
  23. Parse transformation
  24. Python transformation
  25. Rank transformation
  26. Router transformation
  27. Rule Specification transformation
  28. Sequence Generator transformation
  29. Sorter transformation
  30. SQL transformation
  31. Structure Parser transformation
  32. Transaction Control transformation
  33. Union transformation
  34. Velocity transformation
  35. Verifier transformation
  36. Web Services transformation

Transformations

Transformations

Normalized fields

Normalized fields

Define the fields to be normalized on the
Normalized Fields
tab. You can also include other incoming fields that you want to use in the mapping.
When you define normalized fields, you can create fields manually or select fields from a list of incoming fields. When you create a normalized field, you can set the data type to String or Number, and then define the precision and scale.
In an
elastic mapping
, you can use any primitive data type.
When incoming fields include multiple-occurring fields without a corresponding category field, you can create the category field to define the occurs for the data. For example, to represent three fields with different types of income, you can create an Income category field and set the occurs value to 3.