Table of Contents


  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Connections
  4. Mappings in the Hadoop Environment
  5. Mapping Objects in the Hadoop Environment
  6. Processing Hierarchical Data on the Spark Engine
  7. Stateful Computing on the Spark Engine
  8. Monitoring Mappings in the Hadoop Environment
  9. Mappings in the Native Environment
  10. Profiles
  11. Native Environment Optimization
  12. Data Type Reference
  13. Complex File Data Object Properties
  14. Function Reference
  15. Parameter Reference

Targets in a Hadoop Environment

Targets in a Hadoop Environment

You can push a mapping to the Hadoop environment that includes a target from the native environment or from the Hadoop environment. Some sources have limitations when you reference them in the Hadoop environment.
You can run mappings with the following targets in a Hadoop environment:
  • Complex files
  • Flat file (native)
  • Greenplum
  • HBase
  • HDFS flat file
  • Hive
  • IBM DB2
  • Netezza
  • ODBC
  • Oracle
  • Sqoop targets
  • Teradata
A mapping that runs with the Spark engine can have partitioned Hive target tables and bucketed targets.
When a mapping runs in the Hadoop environment, an HDFS target or a Hive target cannot reside on a remote cluster. A remote cluster is a cluster that is remote from the machine that the Hadoop connection references in the mapping.

Updated December 13, 2018