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Data Management

This comprehensive data management course covers a wide range of topics, including data delivery tools, challenges, refinery, high-performance computing clusters, and governance. Participants will explore industry-leading tools like OvalEdge, TrueDat, and IBM Data Governance. They'll also delve into crucial areas such as data operations, automation, lineage, integrity, virtualization, and federation. Engaging class work, including a real-life case study, allows participants to exercise their data refinery skills and table normalization.


Through informative lectures and practical exercises, this course provides a solid foundation in modern data management. Graduates will possess the knowledge and expertise to effectively manage data in today's dynamic business landscape, ready to apply their skills to real-world scenarios.

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Targeted Groups:

This course was designed to fit everyone including: 

  • Data Analysts and Data Scientists

  • Data Managers and Data Stewards

  • IT Professionals

  • Business Intelligence Professionals

  • Database Administrators

  • Data Governance Professionals

  • Business Analysts

  • Project Managers

  • Data Consultants

  • Researchers and Academics

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Course Objectives:

This course was designed to let the participants able:

  • Explain the tools and techniques employed in modern data management at a comprehensive level.

  • Apply acquired knowledge effectively in real-world scenarios to address data management challenges.

  • Explain a range of concepts, including data delivery tools, data refinery, high-performance computing clusters, and data governance.

  • Deploy prominent tools in the field, such as OvalEdge, TrueDat, IBM Data Governance, SnowFlake, Confluent/Kafka, and Plotly.

Course Contents

Unit1: Introduction to Data Management

  • Definition of data management

  • Importance of data management

  • Key concepts and terminology

  • Implementing a data management strategy

  • Evaluating the effectiveness of a data management strategy


Unit 2: Data Modeling

  • Data modeling concepts

  • Data modeling techniques

  • Data modeling tools

  • Class-work-1 - Data Refinery Activity

  • Part I: Identify the Entities and their attributes - Case Study.


Unit 3: Data Delivery

  • Data Delivery – Overview

  • Data delivery tools

  • Challenges is Data Delivery


Unit4: Data Governance

  • Data Governance Concepts

  • Data Governance Tools

  • Factors to consider when selecting a data governance tool.

  • Challenges in Data Governance


Unit 5: Data Operation

  • Principles of Data Operations

  • Tools for Data Operations

  • DataOps vs DevOps

  • Class-work-2 - Data Refinery Activity

  • Part II: Create tables their attributes and Build Relationships


Unit 6: Data Integrations

  • Methods in Data Integrations

  • Using Data Modelling for Data Virtualization Design

  • Data Federation

  • Class-work-3 - Normalization of Tuples

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