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

Hierarchical to hierarchical data processing

Hierarchical to hierarchical data processing

In a mapping that converts hierarchical data to hierarchical data, you can read from one or more hierarchical data sources and write to one hierarchical data file.
You can convert hierarchical input from one schema to a different schema. You can read data from primitive fields, structs, and arrays and arrange the data in a different structure.
You can also transform the data that you are converting. You can join data sources, configure group by and order by fields, filter for specific information, and aggregate incoming and output data.
The following image shows an example of an elastic mapping that uses a Hierarchy Processor transformation to convert hierarchical data to hierarchical data of a different structure:
The mapping shows a source connected to a Hierarchy Processor transformation connected to a target. The Hierarchy Processor transformation is selected in the Mapping Designer, and the Hierarchy Processor tab is selected. The Incoming Fields panel shows one input group with the following fields: CompanyName (string) and Orders (array). The Orders array contains the OrderPrice, OrderDate, Street, City, State, Country, and ZipCode fields. The Output Fields panel contains one output group with the following fields: Company Name (string), Orders (array), and TotalOrderPrice (double). The Orders array contains the following fields: OrderPrice (double), OrderDate (string), Items (array), and OrderAddress (struct).
In this mapping, the data source is a JSON file that contains orders and items data. The data flows into a different JSON file that contains order information. The Hierarchy Processor transformation is selected, and the
Hierarchy Processor
tab shows the structure of the incoming and output data.