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. Data Services transformation
  9. Deduplicate transformation
  10. Expression transformation
  11. Filter transformation
  12. Hierarchy Builder transformation
  13. Hierarchy Parser transformation
  14. Hierarchy Processor transformation
  15. Input transformation
  16. Java transformation
  17. Java transformation API reference
  18. Joiner transformation
  19. Labeler transformation
  20. Lookup transformation
  21. Machine Learning transformation
  22. Mapplet transformation
  23. Normalizer transformation
  24. Output transformation
  25. Parse transformation
  26. Python transformation
  27. Rank transformation
  28. Router transformation
  29. Rule Specification transformation
  30. Sequence Generator transformation
  31. Sorter transformation
  32. SQL transformation
  33. Structure Parser transformation
  34. Transaction Control transformation
  35. Union transformation
  36. Velocity transformation
  37. Verifier transformation
  38. Web Services transformation

Transformations

Transformations

Hierarchical to relational data processing

Hierarchical to relational data processing

In a mapping that converts hierarchical data to relational output, you can process one hierarchical input group and write the data to multiple relational output groups. The output data can be written as normalized relational data or to delimited flat files.
The following image shows an example mapping:
The mapping contains a Source transformation that is connected to a Hierarchy Processor transformation that is connected to two Target transformations.
In this mapping, the data source is a complex file containing customer and order data. The data flows into two relational files: a file with customer data and a file with order data.

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