Table of Contents

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  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. Appendix C: Function Reference

Using an Intelligent Structure Model in a Mapping

Using an
Intelligent Structure Model
in a Mapping

To use an
intelligent structure model
in a mapping, you associate it with a data object that represents a complex file source when you create the object. The data object can be used to process files that are similar to the one used to create the model.
When you create the data object, you either select a model that you created and exported in Cloud
Data Integration
or select an XML, JSON, ORC, Avro, or Parquet sample file.
Intelligent Structure Discovery
creates the model based on the sample file that you select.
You can add a Read transformation based on the data object to a mapping. If you want to process the data any further, such as checking data quality, or structuring the data into relational format, you add relevant downstream transformations to the mapping.
When the mapping runs, the Read transformation reads one or more input files and parses the data into fields, arrays, and structs.
Depending on the model and input, the data object output might contain primitive data types, complex data types, or nested data types. For more information about working with these data types in transformations, see Complex Data Types.
If the input contains unidentified data,
Intelligent Structure Discovery
arranges the output of unidentified data in the sample file in structured JSON format.


Updated September 28, 2020