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

Unmatched groups of multiple-occurring fields

Unmatched groups of multiple-occurring fields

You can normalize more than one group of multiple-occurring fields in a Normalizer transformation. When you include more than one group and the occurs values do not match, configure the mapping to avoid validation errors.
Use one of the following methods to process groups of multiple-occurring fields with different occurs values.
Write the normalized data to different targets
You can use multiple-occurring fields with different occurs values when you write the normalized data to different targets.
For example, the source data includes an Expenses field with four occurs and an Income field with three occurs. You can configure the mapping to write the normalized expense data to one target and to write the normalized income data to a different target.
Use the same occurs value for multiple occurring fields
You can configure the multiple-occurring fields to use the same number of occurs, and then use the generated fields that you need. When you use the same number of occurs for multiple-occurring fields, you can write the normalized data to the same target.
For example, when the source data includes an Expenses field with four occurs and an Income field with three occurs, you can configure both fields to have four occurs.
When you configure the Normalizer field mappings, you can connect the four expense fields and the three income fields, leaving the unnecessary income output field unused. Then, you can configure the mapping to write all normalized data to the same target.