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

Back Next

Big Data Process

As part of a big data project, you collect the data from diverse data sources. You can perform profiling, cleansing, and matching for the data. You build the business logic for the data and push the transformed data to the data warehouse. Then you can perform business intelligence on a view of the data.

Based on your big data project requirements, you can perform the following high-level tasks:

Collect the data.

Cleanse the data

Transform the data.

Process the data.

Monitor jobs.

Introduction to Informatica Big Data Management

Step 1. Collect the Data

Step 2. Cleanse the Data

Step 3. Transform the Data

Step 4. Process the Data

Step 5. Monitor Jobs