Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. B2B transformation
  7. Aggregator transformation
  8. Cleanse transformation
  9. Data Masking transformation
  10. Data Services transformation
  11. Deduplicate transformation
  12. Expression transformation
  13. Filter transformation
  14. Hierarchy Builder transformation
  15. Hierarchy Parser transformation
  16. Hierarchy Processor transformation
  17. Input transformation
  18. Java transformation
  19. Java transformation API reference
  20. Joiner transformation
  21. Labeler transformation
  22. Lookup transformation
  23. Machine Learning transformation
  24. Mapplet transformation
  25. Normalizer transformation
  26. Output transformation
  27. Parse transformation
  28. Python transformation
  29. Rank transformation
  30. Router transformation
  31. Rule Specification transformation
  32. Sequence Generator transformation
  33. Sorter transformation
  34. SQL transformation
  35. Structure Parser transformation
  36. Transaction Control transformation
  37. Union transformation
  38. Velocity transformation
  39. Verifier transformation
  40. Web Services transformation

Transformations

Transformations

Hierarchy Processor transformation overview

Hierarchy Processor transformation overview

You can choose from several different processing strategies to meet your needs, depending on the format of the source data and your desired output format.
The Hierarchy Processor transformation can operate in the following modes:
  • Hierarchical to relational. Converts one hierarchical input group to multiple output groups, which can include delimited flat files or relational files.
  • Relational to hierarchical. Converts up to five relational input groups to one hierarchical output group.
  • Hierarchical to hierarchical. Converts one or more hierarchical input groups to one hierarchical output group with a different schema.
  • Hierarchical to flattened. Converts one hierarchical input group to one flattened denormalized output group.
The default output format is relational.
The data that you pass to or from the Hierarchy Processor transformation requires a Microsoft Azure Data Lake Store V2 or an Amazon S3 V2 connection.

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