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Design a Scalable Data Model

Explore how to design scalable data models using the star schema, a popular approach for organizing data warehouses and lakehouses. Understand the roles of fact and dimension tables, their structure, and how this design enables efficient querying and analysis. Gain practical insight into building a clean and scalable data model for large datasets.

When we talked about where data lives—whether in a data warehouse or a lakehouse—we took a big step in understanding how data flows and is stored. Now, imagine you’re designing a map to navigate through all that stored data. This map helps data engineers and analysts find the answers they need without getting lost.

That’s where the star schema comes in. It’s like creating a well-marked star-shaped map for your data, making it easy to explore and analyze.

The star schema is also known as the Kimball data modelhttps://www.owox.com/blog/articles/star-schema-explained because it was developed by Ralph Kimball in the 1990s. The star schema helps store large datasets efficiently while maintaining historical data.

Enter the star schema

The star schema is one of the easiest and most popular ways to structure a data warehouse. It’s called a star because its layout looks like one—there’s a central table with several other tables branching out from it.

Star schema
Star schema

This schema helps us organize large amounts of data in a way that’s both clear and fast for analysis.

At its core, the star schema includes a central fact table and multiple dimensional tables that connect to it. This structure helps us ask powerful questions about our data ...