IBM InfoSphere DataStage Essentials (v11.5) Course Overview

To ensure you can gain the maximum benefit from the IBM InfoSphere DataStage Essentials (v11.5) course, it is recommended that you meet the following minimum prerequisites:

• Basic understanding of database principles, including SQL.
• Familiarity with Windows operating system environments.
• An introductory knowledge of data warehousing and data modeling concepts.
• Some experience with ETL (Extract, Transform, Load) operations is helpful but not mandatory.
• Basic knowledge of job scheduling and data processing.

These prerequisites are designed to provide a foundation that will help you to more effectively assimilate the course content. They are not intended to be barriers to entry but rather to ensure you are prepared for the technical depth of the training. If you are new to any of these areas, we recommend self-study or introductory courses to get up to speed and make the most of the DataStage Essentials training.

Target Audience for IBM InfoSphere DataStage Essentials (v11.5)

1. The IBM InfoSphere DataStage Essentials course is designed for professionals seeking expertise in data integration and ETL processes.

   
   

2. Target Audience for IBM InfoSphere DataStage Essentials (v11.5):

   
   

• Data Integration Specialists
• ETL Developers
• Data Engineers
• DataStage Administrators
• Data Architects
• Business Intelligence Professionals
• IT Project Managers involved in data handling
• Data Analysts seeking to upgrade their skills
• Database Administrators looking to expand their ETL toolset
• Data Warehousing Specialists
• Technical Consultants involved in data transformation projects
• System Administrators with a focus on IBM InfoSphere environments

Learning Objectives - What you will Learn in this IBM InfoSphere DataStage Essentials (v11.5)?

Introduction to Learning Outcomes:

This IBM InfoSphere DataStage Essentials course equips learners with fundamental skills to build, deploy, and administer DataStage solutions, enabling data integration across complex enterprise environments.

Learning Objectives and Outcomes:

• Understand the architecture and deployment options of DataStage, ensuring effective implementation within an IT infrastructure.
• Acquire necessary administrative skills for DataStage, including project management and environmental configuration.
• Gain expertise in handling metadata within DataStage, essential for data mapping and lineage tracing.
• Learn to create, design, and develop robust parallel jobs to process high volumes of data efficiently.
• Master techniques for accessing and processing sequential data, a key skill for integrating various data formats.
• Understand and apply partitioning and collecting algorithms to optimize data parallelism and performance.
• Develop skills to combine data from multiple sources, ensuring accurate and meaningful information for analysis.
• Learn to use group processing stages to perform complex data transformations and business logic.
• Become proficient with the Transformer stage to perform advanced data manipulation and business rules application.
• Explore repository functions for job versioning, impact analysis, and job control, enhancing data governance and operational control.
• Develop the ability to work with relational data, including database read and write operations, to facilitate data exchange with RDBMS.
• Acquire skills to manage and control DataStage jobs, ensuring proper execution, scheduling, and monitoring of data integration tasks.