Learning Dimensional Data Modeling Masterclass

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Free Download Dimensional Data Modeling Masterclass
Published 2/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 4h 14m | Size: 3.43 GB
Lets design and create a dimensional model together.

What you'll learn
Understand the purpose and rationale behind a dimensional data model
Learn how to follow the Kimball methodology
Hands on design from start to finish, from source data through a Dimensional Model to a visualization
Gain a deep understanding of SCDs, surrogate keys, date dimension, fact types and dimension types
Requirements
A very basic understanding of database design, eg tables, columns and relationships.
Description
A super practical, hands-on course that steps you through the de facto standard for any analytical layer. Learn the reason for using the Kimball approach, and why it is still very relevant today. Build data marts based on your design, incorporating a slowly-changing dimension, date dimension, and role-playing dimension, as well as 2 types of facts. (transactional and snapshot). We will visualise our data marts and refer back to the original business requirement.
Data modeling is the bridge between the Business world and the Technical teams. This source will should you how to bridge that divide through star-schema, facts and dimensions.
Whether your architecture involves an Enterprise Data Warehouse, Delta Lake, Medallion or Data Vault, you will find that a Dimension Layer is the gold standard for further AI models, analytics and reporting.
Be ready to design, build and discover the rationale behind the various design choices, and learn how to apply this thinking in your own environment.
I will step you through installing a MySQL database, use free data modeling software, and write SQL scripts to transform and load data, thereby creating your very own data marts.
This course will give you a solid foundation, and set you up for greater things.
Who this course is for
Aspiring data modelers, BI analysts, Power BI developers, data engineers who would like to understand the reasoning behind the model, data architects

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