Master Dimensional Data Modeling with Claude Code

Dimensional data modeling is one of the foundational skills that every analytics engineer needs to know. However, I didn’t start my career knowing anything about this.

Like most people, I started by learning modern data stack tools, looking for the best tool to solve a certain problem. While that worked at the time, I quickly realized the importance of understanding the principles outside of any tool.

Now, with AI, data modeling has become even more important as it applies business context to data, allowing analytics engineers to validate expectations BEFORE AI can use it to answer business questions.

Without data modeling, there is nuance that an AI model can’t figure out on its own, leading to data quality issues and incorrect assumptions.

Unfortunately, free datasets on the internet don’t mimic actual business problems, making data modeling hard to master. Luckily, our Data Pipeline Summer Aloha Fresh project does. The problem we’ll solve in this part’s challenge looks similar to problems I’ve faced in my full-time analytics engineering roles.

In the last part of Data Pipeline Summer, we learned how to create an AI-ready data warehouse, focusing on protecting raw data and PII while also optimizing for performance.

We loaded data for Aloha Fresh into ClickHouse and explored the raw data tables so we could model them this week.

If you missed that, be sure to check it out here so you can get your project up to speed.

Dimension modeling delivers on two things- fast query performance and data that’s understandable by the business. Simplicity, speed, and usability.

With data, it’s common for projects to grow and grow in complexity until you realize you have a huge bowl of spaghetti that you’re somehow supposed to use to make quick insights. Dimensional data modeling exists to reduce this possible complexity.

To do this, the technique focuses on building tables as facts or dimensions. A row in a fact table represents a measurable event at a certain grain. A row in a dimension describes something. Together, these tables form a star schema.

Separating your data into facts puts business processes at the forefront of your data modeling strategy. With the business processes separated into their own tables, it reduces the complexity while also allowing for querying and “drilling down” across dimensions. You can think of drilling down as the aggregation of events across a dimension.