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
- 10.5.4
- 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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Processing Big Data on Partitions

You can run a Model repository mapping with partitioning to increase performance. When you run a mapping configured with partitioning, the Data Integration Service performs the extract, transformation, and load for each partition in parallel.

Mappings that process large data sets can take a long time to process and can cause low data throughput. When you configure partitioning, the Data Integration Service uses additional threads to process the session or mapping which can increase performance.

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Table of Contents

Big Data Management User Guide

Big Data Management User Guide

Processing Big Data on Partitions

Processing Big Data on Partitions