Mix and Match Data

In the real world, data doesn’t come wrapped in a perfect little bow. It’s all over the place—users in one table, orders in another, and maybe products in a third. On their own, these tables don’t tell the full story. But together? They can show how customers behave, what’s selling, and what’s going wrong. As a data engineer, your job is to bring that story to life by connecting the dots.

But hold on—you don’t just go joining tables like mixing all the leftovers in the fridge. First, you’ve got to understand how tables are built, and how they relate to each other. That means getting friendly with primary keys, foreign keys, and SQL joins—your go-to toolkit for turning scattered pieces into a clean, connected dataset ready for pipelines, analytics, or whatever comes next.

Creating tables in SQL

In SQL, a table is like a dataset: rows represent records, and columns represent fields (attributes). As data engineers, you’ll often create or manage these tables when building pipelines or staging raw inputs.

Here’s a simple syntax to define a table: