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

Complex File Sources on MapR-FS

Complex File Sources on MapR-FS

Use a PowerExchange for HDFS connection to read data from MapR-FS data objects.
The following table shows the complex files that a mapping can process within MapR-FS storage in the Hadoop environment:
File Type
Supported Formats
Supported Engines
Avro
  • Flat
  • Hierarchical
    1 2
  • Blaze
  • Spark
JSON
  • Flat
    1
  • Hierarchical
    1 2
  • Blaze
  • Spark
ORC
  • Flat
  • Spark
Parquet
  • Flat
  • Hierarchical
    1 2
  • Blaze
  • Spark
1
To run on the Blaze engine, the complex file data object must be connected to a Data Processor transformation.
2
To run on the Spark engine, the complex file read operation must be enabled to project columns as complex data type.


Updated September 28, 2020