Data Modeling for Analytics Engineering

This course is designed to give you a solid foundation in data modeling, from core concepts to a step-by-step process that you can apply on the job.

If analytics engineering is about transforming raw data into clean, reliable datasets, then data modeling is the blueprint. It's how you decide what to build, how to structure it, and how to make sure it holds up as your data changes over time.

We'll start by introducing the key building blocks — data models, entities, and relationships — and draw a clear line between data modeling in general and dimensional modeling specifically.

From there, we'll break down the core unit of any data model: the table. You'll learn the main characteristics of tables in a database, then explore the two types you'll find in dimensional modeling — fact tables and dimension tables — and what makes each one unique.

Next, we'll zoom out and look at how those tables fit together. We'll introduce popular schema designs, like the star schema, and talk about how to arrange your facts and dimensions in a way that's clean and easy to query.

But a data model isn't static — data changes, and your model needs to handle that. We'll cover strategies for managing those changes, including slowly changing dimensions, so your models stay accurate without breaking downstream reports.

Last but not least, we'll put it all into practice. You'll see how data flows through a modern analytics workflow, and then walk through the full data modeling process — from scoping requirements to designing and transforming tables — just like an analytics engineer would on the job.

Whether you're building your first dimensional model or looking to formalize your approach, this course will give you the theory and process to do so with confidence.