CF

Dimensional Data Modeling

1h 37m 57s
English
Paid

In today's world, where data plays a crucial role, effective organization of information is the foundation for quality analytics and report building. Multidimensional data modeling is an essential approach that structures data for quick access and informed decision-making.

Course Overview

This course provides a comprehensive introduction to the fundamental concepts of dimensional modeling. You will learn the mechanics of fact and dimension tables, understand what slowly changing dimensions (SCD) are, and explore the different types of fact tables.

Throughout the course, you will gain practical experience in setting up and working with a data warehouse using real-world tools like DuckDB and DBeaver. By the end of the training, you will be well-equipped to design a data model for high-performance analytics and reporting.

What You Will Learn

Introduction to Data Warehouses

Learn the fundamentals of building data warehouses and understand their importance in analytical processing. Discover how a data warehouse consolidates information from various sources to enable fast and scalable analysis.

Basics of Dimensional Modeling

Get acquainted with key elements such as dimensional and fact tables. Learn how to design a data structure for analytics, determine business metrics, identify suitable dimensions, and effectively link them together. Through practical examples, master the principles of building a meaningful and efficient data model.

Setting Up a Data Warehouse

Gain hands-on experience with DuckDB and DBeaver: create and manage tables, and prepare the environment for analytics. This module includes step-by-step instructions for setting up the environment and deploying your first data warehouse.

Working with a Data Warehouse

Delve into advanced topics such as managing slowly changing dimensions (SCD) and working with different types of fact tables, including transactional and cumulative. Understand how to track business events and trends, optimize queries, and analyze large volumes of data.

About the Author: Eka Ponkratova

Eka Ponkratova thumbnail

Eka Ponkratova is a data engineer and educator focused on the modeling side of data warehouse construction — the dimensional and relational schema-design decisions that lock in early in a data warehouse's life and become expensive to change later.

Her CourseFlix listing carries two Eka Ponkratova courses: Dimensional Data Modeling (the Kimball-style star and snowflake schema patterns underneath analytical data warehouses) and Relational Data Modeling (the OLTP-side normalisation and integrity patterns). Together the courses cover both halves of a data engineer's modeling craft.

Material is paid and aimed at data engineers and analytics engineers responsible for the schema decisions in their organisation's data stack. For broader content, see CourseFlix's Data processing and analysis category page.

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#1: Introduction
All Course Lessons (16)
#Lesson TitleDurationAccess
1
Introduction Demo
01:58
2
Course Goals
02:06
3
Intro to Data Warehousing
06:43
4
Approaches to building a data warehouse
05:21
5
Dimension tables explained
05:35
6
Fact tables explained
06:35
7
Identifying dimensions
03:17
8
What is duckdb
05:59
9
First DuckDB hands-on
02:21
10
Creating tables in duckdb
02:41
11
Installing dbeaver
06:50
12
Exploring scd0 and scd1
19:58
13
Exploring scd2
13:53
14
Exploring transaction fact table
06:29
15
Exploring accumulating fact table
07:18
16
Conclusion
00:53
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Frequently asked questions

What prerequisites should I have before taking this course?
Before enrolling in the Dimensional Data Modeling course, it's beneficial to have a basic understanding of data concepts and familiarity with SQL queries. This foundational knowledge will help you grasp the course content more effectively, particularly when working with tools like DuckDB and DBeaver.
What projects or practical exercises are included in the course?
The course includes practical exercises like setting up and working with a data warehouse using DuckDB and DBeaver. Students will create tables in DuckDB and explore different types of dimension and fact tables, including transaction and accumulating fact tables. These hands-on activities are designed to reinforce the theoretical concepts covered.
Who is the target audience for this course?
This course is designed for data analysts, data engineers, and business intelligence professionals who want to deepen their understanding of dimensional data modeling and its application in building efficient data warehouses for analytics and reporting.
How does the depth of this course compare to other data modeling courses?
The course offers a comprehensive introduction to dimensional modeling, focusing on practical application using tools like DuckDB and DBeaver. It covers key concepts like fact and dimension tables, slowly changing dimensions, and data warehouse setup, providing a solid foundation for beginners and a refresher for more experienced professionals.
What specific tools will I learn to use during this course?
During the course, you will gain hands-on experience with DuckDB and DBeaver. DuckDB is used for creating and managing tables, while DBeaver serves as a database management tool to explore and manipulate data structures. These tools are integral to the practical exercises included in the course.
What topics are not covered in this course?
The course does not cover advanced data modeling techniques such as data vault modeling or data lake architectures. Additionally, it does not delve into the use of cloud-based data warehouse solutions or advanced SQL optimization techniques.
How much time should I expect to dedicate to this course?
The course consists of 16 lessons, with each lesson designed to offer a mix of theoretical and practical knowledge. While the exact runtime is not specified, students should allocate sufficient time for both the lessons and hands-on activities to fully benefit from the course material.