What Is SQL and What Is It Used For?

Short answer: SQL (Structured Query Language) is the standard language for storing, reading and changing data in a relational database. You use it to ask questions of your data, such as "which customers ordered this month?", and to add, update or remove records. Apps, analytics teams and AI engineers all rely on it.

What Is a Relational Database?

A relational database stores data in tables, much like spreadsheets. Each table has columns (the kinds of information) and rows (the individual records). Tables can be linked to each other, for example an orders table pointing to a customers table. That is why it is called "relational". Common relational databases include PostgreSQL, MySQL, MariaDB and SQLite, and they all understand SQL with small differences.

An Example Table

Imagine a table called products:

The Four Core Statements

SELECT: read data

SELECT name, price
FROM products
WHERE category = 'stationery'
ORDER BY price DESC;

This returns Notebook (5) and then Pen (2). WHERE filters rows and ORDER BY sorts them.

INSERT: add a row

INSERT INTO products (id, name, category, price)
VALUES (4, 'Mouse', 'electronics', 15);

UPDATE: change existing rows

UPDATE products
SET price = 6
WHERE name = 'Notebook';

DELETE: remove rows

DELETE FROM products
WHERE name = 'Pen';

Always include a WHERE clause with UPDATE and DELETE. Without it, the statement affects every row in the table.

Bonus: counting with GROUP BY

SELECT category, COUNT(*) AS items, AVG(price) AS avg_price
FROM products
GROUP BY category;

This groups the rows by category and gives, for each group, how many products it has and their average price. With the starting table it returns stationery (2 items, average 3.5) and electronics (1 item, average 40). Questions like this are the heart of data analysis, and SQL answers them in a few lines instead of hours of manual counting.

Where Is SQL Used?

• Applications: most websites and apps keep users, orders and messages in a database and read them with SQL behind the scenes.

• Analytics: analysts use GROUP BY and aggregates to count, sum and compare, for example sales per category.

• AI data preparation: training and evaluation data often lives in databases. SQL is how you select, filter and clean it before it reaches a model.

Why Do AI Engineers Need SQL?

AI systems are only as good as their data. An AI engineer often has to pull the right records, join several tables, remove duplicates and check data quality. Many AI applications also store conversations, documents and results in a database. SQL is the tool for all of this, and it is quick to learn. See the AI engineer roadmap to see where it fits.

To go deeper, read the chapter Data, Databases and SQL: Why AI Engineers Need Them and then INSERT, UPDATE, DELETE and Upserts Safely.

Common Mistakes

• Forgetting WHERE on UPDATE or DELETE, and changing every row.

• Using SELECT * everywhere. Ask only for the columns you need.

• Testing on real data. Practise on a copy or a practice database first.

• Interview tip: be ready to explain the difference between WHERE (filters rows before grouping) and HAVING (filters groups after).

Key Takeaway

SQL is the language for talking to relational databases. Learn SELECT, INSERT, UPDATE and DELETE first and you can already do a great deal.

Ready to start? Learn SQL with hands-on practice, from your first query to joins and data cleaning. Begin with the free course SQL and Data Handling for AI: Beginner.