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

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 October 23, 2019