SQL CHEAT SheetSQL Cheat Sheet: Essential Commands, Clauses, and Patterns for Data Analysts

Structured Query Language (SQL) is the backbone of data analysis, data engineering, and business intelligence. Whether you're querying a relational database, building ETL pipelines, or exploring datasets in Power BI, SQL is the language that lets you interact with data efficiently and precisely.

This cheat sheet is designed to be your quick-access reference for the most commonly used SQL commands, clauses, functions, and patterns. It’s ideal for beginners who want to learn fast, and for experienced analysts who need a refresher or a compact guide.

📌 What Is SQL?

SQL (Structured Query Language) is a domain-specific language used to manage and manipulate relational databases. It allows users to:

Retrieve data (SELECT)
Insert new records (INSERT)
Update existing data (UPDATE)
Delete records (DELETE)
Create and modify tables (CREATE, ALTER)
Control access and permissions (GRANT, REVOKE)

SQL is supported by most relational database systems, including:

Microsoft SQL Server
PostgreSQL
MySQL
Oracle
SQLite

🔍 Basic SQL Syntax

sql

SELECT column1, column2
FROM table_name
WHERE condition
ORDER BY column1 ASC;

🧠 Key Clauses

Clause	Purpose	Example
`SELECT`	Choose columns	`SELECT name, age FROM users`
`FROM`	Specify table	`FROM employees`
`WHERE`	Filter rows	`WHERE age > 30`
`ORDER BY`	Sort results	`ORDER BY salary DESC`
`GROUP BY`	Aggregate rows	`GROUP BY department`
`HAVING`	Filter aggregated results	`HAVING COUNT(*) > 5`
`LIMIT`	Restrict number of rows (MySQL, PG)	`LIMIT 10`
`TOP`	Restrict rows (SQL Server)	`SELECT TOP 5 * FROM sales`

🧩 Filtering Data

WHERE Clause

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SELECT * FROM orders
WHERE status = 'Shipped' AND total > 100;

BETWEEN

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SELECT * FROM payments
WHERE amount BETWEEN 50 AND 500;

IN

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SELECT * FROM customers
WHERE country IN ('Italy', 'France', 'Germany');

LIKE

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SELECT * FROM products
WHERE name LIKE 'A%'; -- Starts with A

🔄 Joining Tables

INNER JOIN

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SELECT orders.id, customers.name
FROM orders
INNER JOIN customers ON orders.customer_id = customers.id;

LEFT JOIN

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SELECT employees.name, departments.name
FROM employees
LEFT JOIN departments ON employees.dept_id = departments.id;

RIGHT JOIN

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SELECT students.name, courses.title
FROM students
RIGHT JOIN courses ON students.course_id = courses.id;

FULL OUTER JOIN

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SELECT *
FROM table1
FULL OUTER JOIN table2 ON table1.id = table2.id;

📊 Aggregation Functions

Function	Description	Example
`COUNT()`	Number of rows	`COUNT(*)`
`SUM()`	Total of values	`SUM(sales)`
`AVG()`	Average value	`AVG(score)`
`MIN()`	Minimum value	`MIN(price)`
`MAX()`	Maximum value	`MAX(age)`

GROUP BY

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SELECT department, COUNT(*) AS total_employees
FROM employees
GROUP BY department;

🧠 Subqueries

Inline Subquery

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SELECT name
FROM customers
WHERE id IN (SELECT customer_id FROM orders WHERE total > 500);

Correlated Subquery

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SELECT name
FROM employees e
WHERE salary > (SELECT AVG(salary) FROM employees WHERE dept_id = e.dept_id);

🛠️ Table Management

CREATE TABLE

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CREATE TABLE users (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    email VARCHAR(100),
    created_at DATE
);

ALTER TABLE

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ALTER TABLE users ADD COLUMN phone VARCHAR(20);

DROP TABLE

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DROP TABLE old_data;

🔐 Data Manipulation

INSERT

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INSERT INTO products (name, price)
VALUES ('Laptop', 1200);

UPDATE

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UPDATE orders
SET status = 'Delivered'
WHERE id = 101;

DELETE

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DELETE FROM users
WHERE last_login < '2023-01-01';

🧠 Window Functions

Window functions allow you to perform calculations across a set of rows related to the current row.

ROW_NUMBER

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SELECT name, ROW_NUMBER() OVER (ORDER BY salary DESC) AS rank
FROM employees;

RANK

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SELECT name, RANK() OVER (PARTITION BY dept_id ORDER BY salary DESC) AS dept_rank
FROM employees;

LEAD / LAG

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SELECT name, salary,
       LAG(salary) OVER (ORDER BY salary) AS previous_salary
FROM employees;

🧠 Common Table Expressions (CTEs)

CTEs improve readability and allow recursive queries.

sql

WITH HighValueOrders AS (
    SELECT * FROM orders WHERE total > 1000
)
SELECT * FROM HighValueOrders WHERE status = 'Shipped';

🧠 Useful Patterns

Find Duplicates

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SELECT email, COUNT(*)
FROM users
GROUP BY email
HAVING COUNT(*) > 1;

Top N per Group

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SELECT *
FROM (
    SELECT *,
           ROW_NUMBER() OVER (PARTITION BY category ORDER BY price DESC) AS rank
    FROM products
) ranked
WHERE rank <= 3;

🧠 Performance Tips

Use indexes on columns used in WHERE, JOIN, and ORDER BY
Avoid SELECT * in production queries
Use EXPLAIN or QUERY PLAN to analyze performance
Limit subqueries and nested joins when possible
Normalize data but denormalize for reporting when needed

Final Thoughts

SQL is a foundational skill for any data professional. Whether you're building dashboards, writing ETL scripts, or exploring datasets, knowing how to write clean, efficient SQL queries is essential.

This cheat sheet is meant to be a living reference—bookmark it, expand it, and adapt it to your database environment. With practice, you’ll be able to write queries that are not only correct, but elegant and optimized.

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