Dimensional Modelling

Overview

Dimensional modelling is a core component of modern business intelligence, providing a foundation for effective reporting, analytics, and data-driven decision-making. This two-day instructor-led course introduces the principles of dimensional modelling and explores how it supports data warehouses and data marts.

Designed for learners with no previous experience of dimensional modelling, the course covers data warehousing concepts, analytical requirements gathering, dimensional design techniques, and physical implementation considerations. You’ll learn how to design fact and dimension tables, explore modern approaches to data movement, and understand how online analytical processing technologies support analytical workloads.

The course is vendor and product independent, giving you a foundation that can be applied across a range of business intelligence and analytics platforms.

Prerequisites

There are no formal prerequisites for this course.

A basic understanding of business intelligence or data concepts may be beneficial, but previous experience of dimensional modelling is not required.

Target audience

This course is designed for:

• Business analysts responsible for gathering analytical requirements and designing dimensional models
• Data professionals working with business intelligence and analytics solutions
• IT professionals responsible for implementing dimensional models and online analytical processing solutions
• Anyone looking to build a strong foundation in dimensional modelling and data warehouse design

Objectives

By the end of this course, you’ll be able to:

• Describe data warehousing concepts and explain the role of dimensional modelling in business intelligence
• Gather and interpret analytical business requirements for dimensional models
• Distinguish between facts, measures, and dimensions when designing analytical solutions
• Design dimensional models using recognised modelling techniques
• Describe physical implementation considerations for dimensional models
• Explain how online analytical processing technologies support analytical reporting and business intelligence
• Apply dimensional modelling techniques to practical business intelligence scenarios

Outline

Introduction to data

• Understand the role of data in business intelligence
• Compare online transaction processing and online analytical processing
• Explore the principles of data warehouse design
• Understand the fundamentals of dimensional modelling
• Explain cardinality and data grain

Introduction to modelling

• Gather analytical business requirements
• Explore conceptual, logical, and physical modelling approaches
• Understand facts and dimensions
• Compare star and snowflake schemas
• Explore relationships between fact and dimension tables
• Consider security requirements within dimensional models

Designing dimension tables

• Design attributes and hierarchies
• Understand business keys and surrogate keys
• Create conformed dimensions
• Design time dimensions
• Implement role-playing dimensions
• Explore slowly changing and rapidly changing dimensions

Designing fact tables

• Define facts and measures
• Compare different types of measures
• Understand additive, semi-additive, and non-additive measures
• Explore different fact table designs
• Understand the role of foreign keys

Advanced dimension techniques

• Design degenerate dimensions
• Explore parent-child dimensions
• Understand bridge tables
• Apply snowflaking where appropriate
• Explore synonym dimensions
• Design mini dimensions
• Understand hot-swappable dimensions
• Explore multi-valued dimensions
• Understand bitmap dimensions
• Design junk dimensions
• Explore step dimensions
• Perform first and last analysis

Data movement

• Introduce extract, transform, and load processes
• Understand the role of staging areas
• Explore the medallion architecture
• Apply data transformation and cleansing techniques
• Understand how metadata supports data quality and governance

Physical storage and aggregation

• Understand aggregation storage techniques
• Explore table partitioning
• Select appropriate data types
• Understand index design considerations
• Explore compression techniques
• Design aggregations for analytical performance
• Compare multidimensional, relational, and hybrid online analytical processing approaches
• Understand modern online analytical processing terminology
• Optimise fact table performance
• Apply indexing strategies within star schemas

Common dimensional modelling mistakes

• Review the twelve most common dimensional modelling mistakes
• Identify approaches for avoiding common design issues
• Apply practical considerations when reviewing dimensional model designs

Exams and assessments

There are no formal exams included in this course.

You’ll reinforce your understanding through instructor-led discussions, scenario-based exercises, and practical activities delivered throughout the course. These activities provide opportunities to apply dimensional modelling concepts and test your understanding in realistic business intelligence scenarios.

Hands-on learning

This course includes:

• Scenario-based exercises that apply dimensional modelling concepts to practical situations
• Guided discussions based on real-world business intelligence scenarios
• Practical activities covering dimensional design techniques and analytical modelling