Duration
24 hours
What you will learn
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.
Training Paths that reference this course are:
Information Analysis – Data Integration Architect
DataStage Integration Developer – New to DataStage Parallel Jobs
DataStage Integration Developer - New to Information Server
DataStage Integration Developer – DataStage Parallel Jobs
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 complete DataStage Essentials course or equivalent and at least one year of experience developing parallel jobs using DataStage.
Course Objectives
- Describe the parallel processing architecture
- Describe pipeline and partition parallelism
- Describe the role and elements of the DataStage configuration file
- Describe the compile process and how it is represented in the OSH
- Describe the runtime job execution process and how it is depicted in the Score
- Describe how data partitioning and collecting works in the parallel framework
- List and select partitioning and collecting algorithms
- Describe sorting in the parallel framework
- Describe optimization techniques for sorting
- Describe sort key and partitioner key logic in the parallel framework
- Describe buffering in the parallel framework
- Describe optimization techniques for buffering
- Describe and work with parallel framework data types and elements, including virtual data sets and schemas
- Describe the function and use of Runtime Column Propagation (RCP) in DataStage parallel jobs
- Create reusable job components using shared containers
- Describe the function and use of Balanced Optimization
- Optimize DataStage parallel jobs using Balanced Optimization
This is a version-specific reference outline. Confirm the current syllabus and delivery arrangements with EIEIO.