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KM400 - IBM InfoSphere Advanced DataStage V8 Training

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Duration: 4 Days

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Course Information

Course Description

​This course works with Information Server V8.5. This course is designed to introduce advanced job development techniques in DataStage V8.5.This course replaces course number (DX455).

Course Objectives

​Describe the parallel processing architecture and development and runtime environments

  • Describe the compile process and the runtime job execution process
  • Describe how partitioning and collection works in the parallel framework
  • Describe sorting and buffering in the parallel framework and optimization techniques
  • Describe and work with parallel framework data types
  • Create reusable job components
  • Use loop processing in a Transformer stage
  • Process groups in a Transformer stage
  • Extend the functionality of DataStage by building custom stages and creating new Transformer functions
  • Use Connector stages to read and write from relational tables and handle errors in Connector stages
  • Process XML data in DataStage jobs using the XML stage
  • Design a job that processes a star schema database with Type 1 and Type 2 slowly changing dimensions
  • List job and stage best practices

Course Audience

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

Course Outline

​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 - Advanced Transformer Logic

Unit 9 - Extending the Functionality of Parallel Jobs

Unit 10 - Accessing Databases (start if there is time)

Unit 11 - Processing XML Data

Unit 12 - Slowly Changing Dimensions Stages

Unit 13 - Best Practices

Course Prerequisites

​You should complete:

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