KM404G – IBM InfoSphere Advanced DataStage: Parallel Framework

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This course is designed to introduce advanced parallel job development techniques in DataStage v11.5. In this course students 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.

Course Length: 3 day(s)

Course Price: $2655 CAD

Available Course Formats:

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom

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Course: KM404G – IBM InfoSphere Advanced DataStage: Parallel Framework

Introduction to the parallel framework architecture

  • Describe the parallel processing architecture
  • Describe pipeline and partition parallelism
  • Describe the role of the configuration file
  • Design a job that creates robust test data

Compiling and executing jobs

  • Describe the main parts of the configuration file
  • Describe the compile process and the OSH that the compilation process generates
  • Describe the role and the main parts of the Score
  • Describe the job execution process

Partitioning and collecting data

  • Understand how partitioning works in the Framework
  • Viewing partitioners in the Score
  • Selecting partitioning algorithms
  • Generate sequences of numbers (surrogate keys) in a partitioned, parallel environment

Sorting data

  • Sort data in the parallel framework
  • Find inserted sorts in the Score
  • Reduce the number of inserted sorts
  • Optimize Fork-Join jobs
  • Use Sort stages to determine the last row in a group
  • Describe sort key and partitioner key logic in the parallel framework

Buffering in parallel jobs

  • Describe how buffering works in parallel jobs
  • Tune buffers in parallel jobs
  • Avoid buffer contentions

Parallel framework data types

  • Describe virtual data sets
  • Describe schemas
  • Describe data type mappings and conversions
  • Describe how external data is processed
  • Handle nulls
  • Work with complex data

Reusable components

  • Create a schema file
  • Read a sequential file using a schema
  • Describe Runtime Column Propagation (RCP)
  • Enable and disable RCP
  • Create and use shared containers

Balanced Optimization

  • Enable Balanced Optimization functionality in Designer
  • Describe the Balanced Optimization workflow
  • List the different Balanced Optimization options.
  • Push stage processing to a data source
  • Push stage processing to a data target
  • Optimize a job accessing Hadoop HDFS file system
  • Understand the limitations of Balanced Optimizations


Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek an understanding of the parallel framework architecture.


You should have:

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

Instructor Led In Classroom

Newcomp can directly deliver  IBM Business Analytics courses for Business Intelligence, Performance Management, and IBM Advanced Analytics through the use of in-class training facilities.

Currently,  in-class courses are offered in Markham, Ottawa, Vancouver, Halifax, and Edmonton. Please note that classes can be added to new areas based on demand.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

Self Paced

Students can receive the same high-quality training, with the same courseware at their own speed and schedule with SPVC.  Individuals with busy schedules can complete a course over a 30-day timeframe at a lower price than in-class or ILO courses. Please note that there is no live interaction with an instructor in this format.