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Home > Training > IBM DataStage > IBM InfoSphere Advanced DataStage - Parallel Framework V9.1 Training

IBM InfoSphere Advanced DataStage - Parallel Framework V9.1 Training

Course#: KM402G

This course is designed to introduce advanced parallel job development techniques in DataStage V9.1. In this course you will develop a deeper understanding of the DataStage architecture, including a deeper understanding of the DataStage development and runtime environments. This will enable you to design parallel jobs that are robust, less subject to errors, reusable, and optimized for better performance.

Overview

This course is designed to introduce advanced parallel job development techniques in DataStage V9.1. In this course you will develop a deeper understanding of the DataStage architecture, including a deeper understanding of the DataStage development and runtime environments. This will enable you to design parallel jobs that are robust, less subject to errors, reusable, and optimized for better performance.

Audience

This advanced course is for experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek an understanding of the parallel framework architecture.

Prerequisites

You should have:

  • Taken DataStage Essentials course or equivalent and
  • At least one year of experience developing parallel jobs using DataStage

Outline of KM402G IBM InfoSphere Advanced DataStage - Parallel Framework V9.1 Training

  • Unit 1 - Introduction to the Parallel Framework Architecture
  • Unit 2 - Compilation and Execution
  • Unit 3 - Partitioning and Collecting Data
  • Unit 4 - Sorting Data
  • Unit 5 - Buffering in Parallel Jobs
  • Unit 6 - Parallel Framework Data Types
  • Unit 7 - Reusable components
  • Unit 8 - Balanced Optimization
We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.
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