Mix and Match Data
Learn how data engineers use SQL joins to combine tables, validate pipelines, and prep data for serious downstream use.
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:
CREATE TABLE table_name (column1 datatype,column2 datatype,...);
For example, a table of customers:
CREATE TABLE Customers (CustomerID INT PRIMARY KEY,Name VARCHAR(100),Email VARCHAR(100));
In this example, CustomerID is marked as a PRIMARY KEY, meaning it will uniquely identify each customer—this is essential when linking this table to others.
Tables are often created by data pipelines, not by hand. But it’s important to know how they work so you can read or debug them when needed.
Understanding primary keys
A primary key uniquely identifies each row in a table. For example, no two customers should share the same CustomerID. The following are key characteristics:
Unique: No duplicates allowed.
Not null: Every row must have a value.
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