IBM InfoSphere QualityStage Essentials - v11 Course Overview

IBM InfoSphere QualityStage Essentials - v11 Course Overview

The IBM InfoSphere Training for QualityStage Essentials v11 is designed to equip learners with the skills needed to ensure high data quality by addressing common data quality issues. Through the course, participants will learn to identify and rectify data contaminants, understand QualityStage architecture, utilize its clients, and effectively develop with QualityStage by importing metadata, constructing and running jobs, and analyzing results.

Key components of the training include investigating data anomalies, standardizing data to conform to business rules, matching records to identify duplicates, and using the survive stage to consolidate matched records into a single, authoritative version. Additionally, the course covers advanced techniques such as two-source matching against reference data.

By the end of the IBM InfoSphere Training, learners will be adept at using QualityStage to maintain the integrity and cleanliness of their organization's data, which is critical for making informed business decisions.

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 QualityStage Essentials - v11 course, it is important that participants meet certain prerequisites. These prerequisites are designed to equip you with the foundational knowledge necessary to grasp the course material effectively. Here are the minimum required prerequisites:

  • Basic understanding of data quality concepts and why data quality is important in business contexts.
  • Familiarity with database principles, including the ability to run simple SQL queries.
  • Knowledge of data warehousing and data management concepts.
  • Experience with the Windows operating system as the course may involve navigating the software on this platform.
  • An introductory level of knowledge in IBM InfoSphere DataStage or any other ETL (Extract, Transform, Load) tool will be beneficial, though not mandatory.

Please note that while these prerequisites represent the minimum required knowledge, any additional experience with data analysis, data integration, or previous exposure to IBM InfoSphere tools will be advantageous. Our course is structured to accommodate learners who have a basic foundational understanding, and we strive to guide all participants towards mastering QualityStage functionalities.

Target Audience for IBM InfoSphere QualityStage Essentials - v11

The IBM InfoSphere QualityStage Essentials v11 course is designed for IT professionals focused on data quality and data management.

  • Data Quality Analysts
  • Data Governance Specialists
  • Data Integration Developers
  • ETL (Extract, Transform, Load) Developers
  • Data Architects
  • Information Quality Engineers
  • Master Data Management Professionals
  • Business Intelligence Specialists
  • Data Warehousing Specialists
  • IT Project Managers overseeing data-related projects
  • Technical Consultants involved in data quality projects
  • Database Administrators with a focus on data quality
  • Data Scientists who need to ensure data quality for analytics

Learning Objectives - What you will Learn in this IBM InfoSphere QualityStage Essentials - v11?

Introduction to Learning Outcomes:

Gain mastery of IBM InfoSphere QualityStage v11 to cleanse data and maintain data quality. Understand QualityStage architecture, develop jobs, analyze, standardize, match, and consolidate data effectively.

Learning Objectives and Outcomes:

  • Identify and list common data quality issues, understanding their impact on business processes.
  • Describe QualityStage architecture and functionalities of various QualityStage clients.
  • Import and manage metadata effectively within QualityStage.
  • Build, execute, and troubleshoot DataStage/QualityStage jobs to improve data quality.
  • Utilize Investigate stage to perform data analysis using Character Discrete, Concatenate, and Word Investigations.
  • Apply the Standardize stage to transform and correct data, using rule sets to ensure consistency.
  • Interpret results from the Standardize stage and manage unhandled data efficiently.
  • Construct QualityStage jobs to detect duplicate records, applying multiple Match passes to refine results.
  • Analyze and enhance Match results to achieve a higher level of data accuracy.
  • Develop Survive jobs to consolidate matched records into a comprehensive master record, ensuring the best version of truth.