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

ORC Data Types and Transformation Data Types

ORC Data Types and Transformation Data Types

ORC file data types map to transformation data types that the Data Integration Service uses to move data across platforms.
The following table lists the ORC file data types that the Data Integration Service supports and the corresponding transformation data types:
ORC File Data Type
Transformation Data Type
Range and Description
BigInt
BigInt
-9223372036854775808 to 9,223,372,036,854,775,807.
Boolean
Integer
TRUE (1) or FALSE (0).
Char
String
1 to 104,857,600 characters.
Date
Date/Time
January 1, 0001 to December 31, 9999.
Double
Double
Precision of 15 digits.
Float
Double
Precision of 15 digits.
Integer
Integer
-2,147,483,648 to 2,147,483,647.
SmallInt
Integer
-32,768 to 32,767.
String
String
1 to 104,857,600 characters.
Timestamp
Date/Time
January 1, 0001 00:00:00 to December 31, 9999 23:59:59.997.
Precision to microsecond.
TinyInt
Integer
-128 to 127.
Varchar
String
1 to 104,857,600 characters.
When you run a mapping on the Spark or Databricks Spark engine to write an ORC file to a target, the Data Integration Service writes the data of the Char and Varchar data types as String.
You can use ORC data types to read and write complex file objects in mappings that run on the Spark engine only.

Unsupported ORC Data Types

The Developer tool does not support the following ORC data types:
  • Map
  • List
  • Struct
  • Union

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