Warehouse or Lakehouse?

As our data grows—not just in size but in variety—it becomes more important to choose the right way to store and work with it. The old-school relational databases we once relied on aren’t always sufficient today. That’s where modern architectures like data warehouses and data lakehouses come in.

In this lesson, we’ll explore both of these options. We’ll look at what they are, how they work, and when one might be a better fit than the other. By the end, we’ll have a clear picture of how to design a storage setup that actually works for the kind of data we’re dealing with and the kinds of insights we want to unlock.

Where clean data lives: Data warehouse

A data warehouse is like a well-organized library for structured data. It’s designed to store information that fits neatly into rows and columns, just like traditional spreadsheets or databases. What makes it powerful is how it's optimized for analytics: we clean, filter, and organize the data before we load it in. This approach is called schema-on-write, which means we define the structure first, and only then does the data enter the system.