Table of Contents


  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

Flat File Targets

Flat File Targets

A mapping that is running in a Hadoop environment can write to a flat file target that is in a native environment.
Consider the following limitations when you configure a flat file target in a mapping that runs in a Hadoop environment:
  • The Data Integration Service truncates the target files and reject files before writing the data. When you use a flat file target, you cannot append output data to target files and reject files.
  • The Data Integration Service can write to a file output for a flat file target. When you have a flat file target in a mapping, you cannot write data to a command.

Updated October 23, 2019