SQL Introduction: What SQL is and how it works

SQL (Structured Query Language) is the standard language used to interact with relational databases.

If you work with data, applications, analytics, or backend systems, SQL is a foundational skill.

This article is Part 01 of a 10-part SQL series, designed for:

• Developers building applications
• Analysts querying and interpreting data
• Beginners who want clarity without jargon

Table of Contents

• What Is SQL?
• How SQL Works
• Relational vs Non-Relational Databases
• SQL vs RDBMS
• Is SQL Case-Sensitive?
• Basic SQL Syntax Examples
• SQL for Developers vs Analysts
• Practice Exercises
• Conclusion
• Solutions to the exercises from the article

What is SQL?

SQL (Structured Query Language) is a declarative programming language used to create, read, update, and delete data stored in relational databases.

💡 One-sentence definition (featured snippet friendly):
SQL is a language that allows you to query and manipulate structured data stored in relational database tables.

Mental Model: How to visualise SQL

• A database → a folder
• A table → a spreadsheet
• A row → one record
• A column → one attribute
• SQL → the language you use to ask questions

Example SQL query

SELECT name
FROM customers
WHERE country = 'UK';

SQL 01: The Fundamentals

What this means in plain English:

1. Look in the customers table
2. Find rows where country is ‘UK’
3. Return only the name column

How SQL works: The Query lifecycle

SQL follows a specific step-by-step process to get you your data:

1. You write a SQL query.
2. The database parses and validates it (checking for syntax errors).
3. The query planner decides how to execute it (finding the fastest path).
4. Tables and indexes are scanned.
5. A result set is returned.

SQL Is declarative

SQL is a declarative language, meaning you describe what you want, not how to get it.

SELECT * FROM products WHERE price > 100;

The database decides:

• Which index to use
• How to scan rows
• How to optimize performance

SQL Sublanguages (Quick Reference)

Relational (SQL) vs. Non-Relational (NoSQL)

Relational databases (SQL)

• Fixed schema
• Tables with rows and columns
• Strong consistency (ACID)
• Excellent for structured data
• Examples: MySQL, PostgreSQL, Oracle, SQL Server

Non-Relational databases (NoSQL)

• Flexible schema
• JSON, key-value, or graph data
• Horizontally scalable
• Designed for high-volume or unstructured data
• Examples: MongoDB, Redis, Cassandra, Neo4j

Key differences

SQL vs. RDBMS

• SQL = The Language: Defines syntax and rules for interacting with data.
• RDBMS = The Software: Relational Database Management Systems that execute SQL and store data (e.g., MySQL, PostgreSQL, SQLite).

🧠 Analogy: SQL is like English; MySQL or PostgreSQL are like phones that let you speak it.

Is SQL case-sensitive?

Basic SQL syntax examples

SELECT – Query data

SELECT * FROM employees;
SELECT first_name, last_name FROM employees;
SELECT * FROM employees WHERE department = 'HR';

INSERT - Add data

INSERT INTO employees (first_name, last_name)
VALUES ('Jane', 'Doe');

UPDATE – Modify data

UPDATE employees
SET department = 'Marketing'
WHERE id = 101;

UPDATE – Modify data

UPDATE employees
SET department = 'Marketing'
WHERE id = 101;

DELETE – Remove data

DELETE FROM employees WHERE id = 101;

SQL for Developers vs. Analysts

SQL for Developers

• Power APIs and backend services
• Enforce data integrity
• Optimize performance & Index tuning
• Work with migrations and transactions

SQL for Data Analysts

• Explore datasets
• Build reports and dashboards
• Use GROUP BY and Aggregations
• Window functions for deep insights

Practice Exercises

Exercise 1 – Identify the parts

Identify the table name, selected columns, and filter condition:

SELECT email FROM users WHERE active = true;

Exercise 2 – Write your first query

Write a query to select all columns from a table called products where the price is greater than 50.

Exercise 3 – Think like SQL

Explain in plain English what this query does:

SELECT name FROM customers ORDER BY created_at DESC;

Conclusion

SQL sits at the intersection of software development, data analysis, and business intelligence. In the next article (SQL 02), we’ll break down tables, rows, columns, and data types.

Happy querying! 🚀

Solutions to the exercises from the article

Exercise 1 – Identify the Parts

Query:

SELECT email FROM users WHERE active = true;

• Table name: users
• Selected columns: email
• Filter condition: active = true (This ensures you only get records for users who are currently marked as active).

Exercise 2 – Write Your First Query

Task:

Select all columns from a table called products where the price is greater than 50.

Solution:

SELECT * FROM products 
WHERE price > 50;

Exercise 3 – Think Like SQL

Query:

SELECT name FROM customers ORDER BY created_at DESC;

Plain English Explanation:

“Go to the customers table, grab the name of every customer, and list them starting from the most recently created (newest) down to the oldest.”

Bonus: How the database “thinks” (The execution order)

While we write SQL in the order of SELECT -> FROM -> WHERE, the database actually executes it in a different order to be efficient:

• FROM: Where is the data? (Find the table)
• WHERE: Which rows do I need? (Filter the data)
• SELECT: Which columns should I show? (Pick the fields)
• ORDER BY: How should I present them? (Sort the results)

Next up: SQL 02 – Tables, Rows, Columns, and Data Types 🚀