Table of Contents

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

Transformations

Transformations

Hierarchical data in advanced mode

Hierarchical data in advanced mode

In advanced mode, you can use hierarchical fields that represent an array, map, or struct.
You can use hierarchical fields as pass-through fields. You can also use complex operators to access a primitive child field and use the child field to perform an aggregate calculation. For example, you can use the dot operator to access an integer in a struct field and pass the integer as an argument to the SUM function. For more information about complex operators, see
Function Reference
.
Consider the following guidelines when you use hierarchical fields:
  • You cannot use a hierarchical field as a group by field.
  • The output of an aggregate calculation cannot be an array, map, or struct.

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