Table of Contents


  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



Reading hierarchical data in an elastic mapping

Reading hierarchical data in an
elastic mapping

You can use a Source transformation in an
elastic mapping
to read hierarchical data from complex files, such as Avro, JSON, and Parquet files. The
elastic mapping
represents the data as an array, map, or struct.
You can use the hierarchical fields as pass-through fields to convert data from one complex file format to another. For example, you can read hierarchical data from an Avro source and write the data to a JSON target. You can also use the hierarchical fields and their child fields in expressions and conditions in downstream transformations. For information about accessing child fields, see the
Function Reference
You can pass hierarchical fields to the following transformations:
  • Target
  • Aggregator
  • Expression
  • Filter
  • Hierarchy Processor
  • Joiner
  • Rank
  • Router
  • Sequence Generator
  • Sorter

Rules and guidelines for reading hierarchical data

Consider the following guidelines when you read hierarchical data:
  • You must use an Amazon S3 V2 or Azure Data Lake Storage Gen2 connection to read hierarchical data. For more information, see the help for the appropriate connector.
  • You cannot use a parameter for the source connection or the source object.
  • If hierarchical fields contain child fields with decimal data types, the
    elastic mapping
    runs using low precision.
  • The transformation sets the precision and scale based on the values in the first row of data. Note that this first row is sometimes referred to as row 0.
  • To avoid data truncation, increase the precision and scale in the first row of data. Also ensure that the first row does not include null values.