Preface
Introduction to Informatica Big Data Management
Mappings in the Hadoop Environment
Mapping Sources in the Hadoop Environment
Mapping Targets in the Hadoop Environment
Mapping Transformations in the Hadoop Environment
Processing Hierarchical Data on the Spark Engine
Configuring Transformations to Process Hierarchical Data
Processing Unstructured and Semi-structured Data with an Intelligent Structure Model
Stateful Computing on the Spark Engine
Monitoring Mappings in the Hadoop Environment
Mappings in the Native Environment
Profiles
Native Environment Optimization
Cluster Workflows
Connections
Data Type Reference
Function Reference
- Function Support in the Hadoop Environment
- Function and Data Type Processing
  - Rules and Guidelines for Spark Engine Processing
  - Rules and Guidelines for Hive Engine Processing
Parameter Reference
- Parameters Overview
- Parameter Usage

Big Data Management User Guide

10.2.1
- 10.5.7
- 10.5.6
- 10.5.5
- 10.5.4
- 10.5.3
- 10.5.2
- 10.5.1
- 10.5
- 10.4.1
- 10.4.0
- 10.2.2 HotFix 1
- 10.2.2 Service Pack 1
- 10.2.2

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Big Data Management Engines

When you run a big data mapping, you can choose to run the mapping in the native environment or a Hadoop environment. If you run the mapping in a Hadoop environment, the mapping will run on one of the following job execution engines:

Blaze engine

Spark engine

Hive engine

For more information about how Big Data Management uses each engine to run mappings, workflows, and other tasks, see the chapter about Big Data Management Engines.

Introduction to Informatica Big Data Management

Blaze Engine Architecture

Spark Engine Architecture

Hive Engine Architecture

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