SQL Data Types
Explained for Beginners

SQL tables store data. But databases need rules for what kind of data can go into each column. That is where SQL data types come in.

If you are learning SQL for data analysis, interviews, or real projects, understanding SQL data types is essential. Because bad data types create:

SQL data types define the kind of data a column can store. Think of them as rules.

CREATE TABLE customers (
   customer_id INT,
   customer_name VARCHAR(100),
   signup_date DATE
);

Beginners often ignore data types. That becomes painful later. Because analytics depends on correct data structure.

Revenue Analysis

Revenue should be numeric.

-- Correct:
revenue DECIMAL(10,2)

-- Wrong:
revenue VARCHAR(50)

Customer Signup Tracking

-- Correct:
signup_date DATE

-- Wrong:
signup_date VARCHAR(20)

Why? Date filtering becomes difficult.

-- Bad:
WHERE signup_date > 'Jan 5'

-- Good:
WHERE signup_date > '2025-01-05'

Product Availability

-- Correct:
is_active BOOLEAN

This makes logic cleaner.

These cover most beginner analytics use cases.

INT stores whole numbers. No decimals.

CREATE TABLE orders (
   quantity INT
);

Valid values: 1, 25, 100, 999
Invalid values: 12.5, abc

Real Analyst Example: Order quantity

CREATE TABLE sales (
   units_sold INT
);

Use case: Inventory analysis.

When to Use INT

VARCHAR stores text. VARCHAR = Variable Character.

customer_name VARCHAR(100)

This means text up to 100 characters. Examples: Rahul, Priya Sharma, Mumbai, Electronics.

Why VARCHAR Matters

Real Analyst Example: Customer info

CREATE TABLE customers (
   email VARCHAR(255)
);

VARCHAR vs CHAR

DATE stores calendar dates. No time. Format usually: YYYY-MM-DD.

signup_date DATE

Valid: 2025-01-15, 2024-12-01

Real Analyst Example: Customer signup analysis

CREATE TABLE customers (
   signup_date DATE
);

SELECT *
FROM customers
WHERE signup_date > '2025-01-01';

BOOLEAN stores true/false values. Examples: TRUE, FALSE.

Real Analyst Example: Customer status

CREATE TABLE users (
   is_premium BOOLEAN
);

SELECT *
FROM users
WHERE is_premium = TRUE;

DECIMAL stores exact decimal numbers. Critical for financial calculations.

price DECIMAL(10,2)

Meaning: total digits = 10, decimal places = 2. Examples: 99.99, 1500.50, 12.75.

Why DECIMAL Matters

Money should not use INT. If you use price INT, you lose decimal precision (cents / paise). 99.99 becomes 99.

Real Analyst Example: Revenue table

CREATE TABLE sales (
   revenue DECIMAL(12,2)
);

SELECT SUM(revenue)
FROM sales;

DATETIME stores both date and time. Format example: 2025-06-01 14:30:00.

Why DATETIME Matters

Real Analyst Example: Orders

CREATE TABLE orders (
   order_timestamp DATETIME
);

SELECT *
FROM orders
WHERE order_timestamp > '2025-06-01 00:00:00';

This is realistic analytics schema design:

CREATE TABLE customers (
   customer_id INT,
   customer_name VARCHAR(100),
   email VARCHAR(255),
   signup_date DATE,
   is_active BOOLEAN
);

CREATE TABLE orders (
   order_id INT,
   customer_id INT,
   revenue DECIMAL(12,2),
   order_timestamp DATETIME
);

Interviewers test practical understanding. Not memorized definitions.

1. What data type should store customer names?
2. What data type should store product price?
3. What data type should store signup date only?
4. What data type should store login timestamp?
5. What data type should store yes/no account status?

What Comes Next?

After SQL data types, learn SQL constraints (primary keys, foreign keys, NOT NULL, UNIQUE, DEFAULT). Because defining what data is only step one. Next comes defining the rules.

SQL data types seem basic. But they shape everything. Good schemas create clean analytics. Bad schemas create constant frustration.

Master these six first. They cover most beginner SQL work.