IBM InfoSphere DataStage v11.5 - Advanced Data Processing Course Overview

IBM InfoSphere DataStage v11.5 - Advanced Data Processing Course Overview

The IBM InfoSphere DataStage v11.5 - Advanced Data Processing course is a comprehensive training program designed to equip learners with advanced skills in data integration and transformation using IBM InfoSphere DataStage. The course covers various techniques for accessing databases, handling unstructured data, implementing data masking strategies, enforcing data quality with data rules, processing XML and hierarchical data, and updating star schema databases in data warehousing scenarios.

Learners will delve into the specifics of connector stages, exploring their functionality, error handling, and use with multiple input links. They will gain proficiency in processing unstructured data, such as extracting information from Excel spreadsheets and handling various data formats. The course also emphasizes the importance of data masking for protecting sensitive information, applying data masking policies, and utilizing reference tables for consistent data anonymization.

Participants will learn to validate data quality using data rules and enhance data governance. Furthermore, they will acquire skills in processing XML data, mastering the Hierarchical Stage to manage complex XML structures, and transforming this data for analytical purposes. Finally, the course provides insights into star schema updates, managing surrogate keys, and handling slowly changing dimensions within data warehousing environments.

By the end of the course, learners will have a robust understanding of advanced data processing techniques that can be applied to real-world data integration challenges, enhancing their proficiency in DataStage and expanding their capabilities in data management and analytics.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

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Course Prerequisites

To ensure a successful learning experience in the IBM InfoSphere DataStage v11.5 - Advanced Data Processing course, students should have the following minimum prerequisites:

  • Basic understanding of database concepts, including tables, relationships, and SQL.
  • Familiarity with the fundamentals of data warehousing concepts.
  • Prior experience with IBM InfoSphere DataStage or completion of an introductory DataStage course (preferably the IBM InfoSphere DataStage v11.5 - Essentials course or equivalent experience).
  • Knowledge of data transformation principles and ETL (Extract, Transform, Load) processes.
  • Ability to navigate the operating system that DataStage is installed on, such as Linux or Windows.
  • Basic understanding of the principles of structured and unstructured data processing.
  • Comfortable with reading and interpreting data models and data flow diagrams.

These prerequisites are intended to provide students with the foundational skills and knowledge required to grasp the advanced topics covered in the course effectively. With these competencies, learners will be well-prepared to engage with the course content and apply the advanced DataStage features in their data integration projects.

Target Audience for IBM InfoSphere DataStage v11.5 - Advanced Data Processing

The IBM InfoSphere DataStage v11.5 - Advanced Data Processing course caters to professionals aiming to master complex ETL processes and data integration techniques.

  • Data Engineers
  • ETL Developers
  • Data Architects
  • BI Professionals
  • Data Analysts
  • Database Administrators
  • IT Managers overseeing data management teams
  • Data Integration Specialists
  • Data Warehousing Specialists
  • Solution Architects involved in data projects
  • Technical Consultants who design and implement data solutions
  • IT professionals aspiring to upskill in IBM DataStage and data processing

Learning Objectives - What you will Learn in this IBM InfoSphere DataStage v11.5 - Advanced Data Processing?

  1. Introduction to the course’s learning outcomes: This advanced course delves into IBM InfoSphere DataStage v11.5 capabilities for processing complex data, including unstructured data, XML, and database operations, emphasizing efficiency and error management.

  2. Learning objectives and outcomes:

  • Understand the usage and configuration of Connector Stages for efficient database access and error handling.
  • Gain proficiency in handling multiple input links and leveraging the File Connector Stage for HDFS file operations.
  • Learn to process unstructured data using the Unstructured Data Stage, including extraction from Excel spreadsheets and specifying data ranges.
  • Master data masking techniques and policies for protecting sensitive data within DataStage jobs.
  • Employ Data Rules within DataStage to validate data integrity and create custom rules for specific validation needs.
  • Acquire skills to process XML data using the Hierarchical Stage, including data composition, transformation, and consumption.
  • Utilize the Hierarchical Data Stage components to manage and transform complex XML and hierarchical data structures.
  • Develop an understanding of updating star schema databases, managing surrogate keys, and implementing slowly changing dimensions (SCDs).
  • Design and execute DataStage jobs that handle Type 1 and Type 2 SCDs within star schema environments.
  • Strengthen skills in enhancing data quality, enforcing data governance, and ensuring data consistency across various data processing scenarios.