Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace. This course is designed to introduce advanced parallel job development techniques in DataStage v11.5. 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. If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. Terms and Conditions: Ingram Micro – https://www.ingrammicrotraining.com/Terms-of-use.aspx; IBM – http://www.ibm.com/training/terms

Version:

v11.5

Audience:

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

Objectives:

Detail:

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

Pre-Requisites:

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