Data Modelling

In this two day ABIS training, participants will learn how to create data models for relational databases, focusing on implementation for OLTP applications.

From understanding the basics of entity-relationship modelling to applying normalization and optimization techniques, participants will gain an understanding of the entire data modelling process.

This course will enable participants

  • to master the techniques needed to build data models
  • to apply key data modeling design principles through both classic entity-relationship notation and the “crow’s foot” notation
  • to build semantically accurate data models consisting of entities, attributes, relationships, hierarchies, and other modeling constructs

Schedule a training?

Delivered as a live, interactive training: available in-person or online, or in a hybrid format. Training can be implemented in English, Dutch, or French.

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Public training calendar

No public sessions are currently scheduled. We will be pleased to set up an on-site course or to schedule an extra public session (in case of a sufficient number of candidates). Interested? Please let us know.

Intended for

A business analyst, data engineer/analyst, or database designer, who needs to build a personal toolbox of data modeling best practices and techniques.

Background

Participants only need a basic understanding of data management concepts and constructs such as relational database tables and how different pieces of data logically relate to one another. No other prerequisites are needed.

Main topics

  1. Introduction to Data Modelling – the need for data modelling
    • Data modelling – essential part of a Bigger Picture
    • Understand the importance of data modelling and its role in database design
  2. Entity-Relationship Modelling (ERD)
    • Creating Entity-Relationship Diagrams (ERDs) to represent the structure and relationships of data
    • Labs
  3. Normalization Techniques
    • The principles of normalization (1NF to 5NF; BCNF); how to apply these techniques to eliminate redundancy and ensure data integrity
    • The issue of denormalization
    • Labs
  4. Conceptual, Logical, and Physical Modelling
    • The differences between conceptual, logical, and physical data models – and the need for all three
    • Creating conceptual, logical data, physical data base models: differences and similarities.
    • Deriving logical data models from conceptual models
    • Deriving physical database models from logical data models
    • The importance of indexes, views, data types, keys, ...
    • Labs
  5. Best Practices and Guidelines
    • Modelling industry best practices – naming conventions, documentation standards, design considerations, ...
    • The importance of standards within the context of corporate data models
    • The impact of enhanced security, auditing and traceability on model development
    • Labs
  6. Data Model Documentation
    • How to document data models effectively, including entity definitions, attribute details, relationships, and constraints
    • Examples
  7. Modelling Notations and tools
    • Modelling notations (Crows Foot, UML, Chen, ...)
    • Modelling tools (ERwin, Oracle SQL Data Modeler, Lucid Chart, SqlDBM, IBM Rational tooling, ...)
    • Demo
  8. What about other application types – an introduction
    • OLTP versus OLAP
    • Dimensional Modelling – data warehouses – analytical databases
    • Star models, snowflake models
    • NoSQL databases
    • Demo
  9. Case study

Training method

Live instructor-led training, with plenty of opportunities for hands-on exercises and discussion.

Certificate

At the end of the session, the participant receives a 'Certificate of Completion'.

Duration

2 days.

Course leader

Peter Vanroose (ABIS)


SESSION INFO AND ENROLMENT