Data Modeling 3 - Dimensional Data Modeling

Introduction

Description

In today’s data-driven world, efficient data organization is key to enabling insightful analysis and reporting. Dimensional data modeling is a crucial technique that helps structure your data for faster querying and better decision-making.

This course provides a comprehensive introduction to the key concepts of dimensional modeling. From fact and dimension tables, to slowly changing dimensions, and different types of fact tables.

It also offers you hands-on experience using real-world tools like DuckDB and Beaver while setting up and working with your own data warehouse. By the end, you’ll have a solid understanding of how to model data for high-performance reporting and analytics.

Introduction to Data Warehousing

Learn the foundations of data warehousing and why it is essential for analytical processing. Understand the role of a data warehouse in consolidating and organizing data from different sources to support high-performance querying and decision-making.

Dimensional Modeling Basics

In this section, you will be introduced to the key components of dimensional modeling, including dimension and fact tables. You'll learn how they work together to structure data for efficient reporting and analysis. Through practical examples, you’ll understand different approaches to building a data warehouse, how to design dimension tables that provide context, and how to create fact tables that capture business metrics. You will also explore the process of identifying the right dimensions to ensure your data is organized for meaningful insights.

Data Warehouse Setup

Get hands-on with setting up your data warehouse using DuckDB and DBeaver. You’ll learn how to create and manage tables in DuckDB, explore its powerful features, and set up your data for analysis. This module focuses on practical setup and configuration, providing step-by-step guidance for preparing your environment.

Working with the Data Warehouse

In this section, you’ll explore advanced topics like handling Slowly Changing Dimensions (SCD) and managing updates to dimension data over time. You’ll work through different types of fact tables, including transaction and accumulating fact tables, to track business events and trends. These hands-on lessons will help you understand how to manage and analyze large datasets in your warehouse, optimizing for high-performance querying and long-term data management.

Requirements

You could review the following related courses in our Academy :

Introduction to Python
Python for Data Engineers
Schema Design Data Stores
Choosing Data Stores

About the Author

Eka Ponkratova

Eka is not only a Data Solutions Consultant. More than that, she is a data enthusiast with the mission to empower small to medium-sized businesses in Sub-Saharan Africa that focus on essential products and services.

For the last six years, she has been living on the go, specializing in kickstarting projects from the ground up, particularly greenfield initiatives.

As a freelancer who has already worked for various companies, data modeling is one of her main passions.

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