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

Structure Parser transformation

Structure Parser transformation

The Structure Parser transformation transforms your input data into a user-defined structured format based on an
intelligent structure model
. You can use the Structure Parser transformation to analyze data such as log files, clickstreams, XML or JSON files, Word tables, and other unstructured or semi-structured formats.
You can connect a Structure Parser transformation to the following types of sources:
  • A Source transformation based on a flat file to process local input files
  • A Source transformation based on a Hadoop Files V2 connection to stream input files in HDFS or to process local input files
When you configure a Structure Parser transformation, you associate it with an
intelligent structure model
. An
intelligent structure model
is an asset that
Intelligent Structure Discovery
generates to represent the data that you expect the model to parse at run time. You can create a model before you configure the Structure Parser transformation or as you configure it.
Intelligent Structure Discovery
generates the
intelligent structure model
based on a sample of your input data or a schema that you provide. You can create a model from the following input types:
  • Text files, including delimited files such as CSV files and complex files that contain textual hierarchies
  • Machine generated files such as weblogs and clickstreams
  • JSON files
  • XML files
  • ORC files
  • Avro files
  • Parquet files
  • Microsoft Excel files
  • Data within PDF form fields
  • Data within Microsoft Word tables
  • XSD files
  • Cobol copybooks
Creating intelligent structure models based on Cobol copybooks is available for preview.
Preview functionality is supported for evaluation purposes but is unwarranted and is not supported in production environments or any environment that you plan to push to production. Informatica intends to include the preview functionality in an upcoming release for production use, but might choose not to in accordance with changing market or technical circumstances. For more information, contact Informatica Global Customer Support.
After
Intelligent Structure Discovery
generates the
intelligent structure model
, you can refine the model and customize the structure of the output data. You can edit the nodes in the model to combine, exclude, flatten, or collapse them.

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