Table of Contents

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  1. Preface
  2. Introduction to Big Data Management Administration
  3. Big Data Management Engines
  4. Authentication and Authorization
  5. Running Mappings on a Cluster with Kerberos Authentication
  6. Configuring Access to an SSL/TLS-Enabled Cluster
  7. Cluster Configuration
  8. Cluster Configuration Privileges and Permissions
  9. Cloud Provisioning Configuration
  10. Queuing
  11. Tuning for Big Data Processing
  12. Connections
  13. Multiple Blaze Instances on a Cluster

Big Data Management Administrator Guide

Big Data Management Administrator Guide

Big Data Management Engines Overview

Big Data Management Engines Overview

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 the Blaze engine, the Spark engine, or the Hive engine.
When you validate a mapping, you can validate it against one or all of the engines. The Developer tool returns validation messages for each engine.
You can then choose to run the mapping in the native environment or in the Hadoop environment. When you run the mapping in the native environment, the Data Integration Service processes the mapping logic. When you run the mapping in the Hadoop environment, the Data Integration Service uses a proprietary rule-based methodology to determine the best engine to run the mapping. The rule-based methodology evaluates the mapping sources and the mapping logic to determine the engine. The Data Integration Service translates the mapping logic into code that the engine can process, and it transfers the code to the engine.
This chapter describes each run-time engine and how it works in a Big Data Management deployment.

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