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

Relational Sources

Relational Sources

Relational sources are valid in mappings that run in a Hadoop environment if you use the Hive engine or the Blaze engine. The Spark engine cannot run mappings with relational resources.
The Data Integration Service does not run pre-mapping SQL commands or post-mapping SQL commands against relational sources. You cannot validate and run a mapping with PreSQL or PostSQL properties for a relational source in a Hadoop environment.
The Data Integration Service can use multiple partitions to read from the following relational sources:
  • IBM DB2
  • Oracle
You do not have to set maximum parallelism for the Data Integration Service to use multiple partitions in the Hadoop environment.


Updated October 23, 2019