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

Relational to hierarchical data processing

Relational to hierarchical data processing

In a mapping that converts relational data to hierarchical output, you can have up to five relational data sources and write to one hierarchical target output file. This transformation allows you to create structs and arrays. It also allows you to join data sources, group by and order by data fields, filter for specific information, and aggregate both the input and output data.
The following image shows the mapping:
The mapping contains three Source transformations that are connected to a Hierarchy Processor transformation that is connected to one Target transformation.
In this mapping, the source input includes three relational files: customer address data, purchase orders, and purchase order details. The data flows into one complex file that combines data from the three source files.