Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings in the Hadoop Environment
  4. Mapping Sources in the Hadoop Environment
  5. Mapping Targets in the Hadoop Environment
  6. Mapping Transformations in the Hadoop Environment
  7. Processing Hierarchical Data on the Spark Engine
  8. Configuring Transformations to Process Hierarchical Data
  9. Processing Unstructured and Semi-structured Data with an Intelligent Structure Model
  10. Stateful Computing on the Spark Engine
  11. Monitoring Mappings in the Hadoop Environment
  12. Mappings in the Native Environment
  13. Profiles
  14. Native Environment Optimization
  15. Cluster Workflows
  16. Connections
  17. Data Type Reference
  18. Function Reference
  19. Parameter 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. The data object can then be used to process files that are similar to the one used to create the model.
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.


Updated October 23, 2019