SQL Data Types: Essential Guide for Beginners

When you first start working with SQL, it’s easy to think of data types as simple labels. A single column stores either numbers, text, dates, or special data types. But a data type is more than just a label. It’s a set of rules that defines what a value is in the eyes of the database.

In practice, data types shape the way SQL “understands” the information in your database. It’s a set of instructions that gives SQL the context it needs to read, compare, and process your data accurately.

In this tutorial, we’ll look at the major SQL data type categories and see how they behave in different database management systems. I encourage you to follow along with the examples throughout.

If you haven’t worked much with database tables before, our SQL Fundamentals course gives a clear, beginner-friendly foundation for understanding how datasets like this are structured.

What Are SQL Data Types?

A data type describes the format of your data: the kinds of values a column can hold, how those values are stored internally, and the operations that make sense to run on them. 

This means your choice of data type affects more than just table design. It can influence storage needs, query performance, sorting behavior, math operations, and even how different systems interpret the same value.

Here’s a simple table that uses several types at once:

CREATE TABLE employees (
    id INT,
    name VARCHAR(50),
    start_date DATE,
    active BOOLEAN,
    salary DECIMAL(10, 2)
);

Each column in this table defines the type of data it can hold.

• The id column will store whole numbers, giving each employee a unique identifier.

• The name column can hold up to 50 characters of text, so you can store short names.

• The start_date column is for calendar dates.

• The active column will store true or false values, making it easy to track which employees are currently active.

• Finally, the salary column will store monetary amounts with a 10-digit precision, keeping two digits after the decimal point for cents.

Together, these columns form a structured table that enforces rules about what kinds of values are allowed in each field. This makes the data more predictable and easier to work with. 

Let’s add some example data to this and see what the resulting table might look like, using PostgreSQL.

INSERT INTO employees (id, name, start_date, active, salary)
VALUES
    (1, 'Alice Johnson', '2025-01-15', TRUE, 55000.00),
    (2, 'Bob Smith', '2024-06-01', FALSE, 62000.50);
    
SELECT *
FROM employees;

Different database systems follow the same principles but vary in naming and features. For example, SQL Server uses BIT instead of BOOLEAN, and MySQL treats BOOLEAN as a small integer.

Categories of SQL Data Types

There are four main categories of data types: numeric, character, date, and special. Let’s look at each in detail.

Numeric data types

Numeric types store whole numbers or decimals. The specific numeric type will determine the range, precision, and storage size of the numbers stored. The most common data types in this category are:

• integers, like INT, SMALLINT, and BIGINT, for whole numbers,

• decimals, like DECIMAL(p, s) and NUMERIC(p, s), for exact decimal values,

• and floats, like FLOAT and DOUBLE, for approximate values that may introduce small rounding differences

Let’s create an example table to demonstrate different numeric data types:

CREATE TABLE products (
    product_id INT,
    price DECIMAL(8, 2),
    measurement FLOAT
);

INSERT INTO products VALUES 
    (1, 19.99, 3.1415),
    (2, 250.56, 2.71828);

SELECT * FROM products;

Here’s what the output table would look like:

As you can see, the first column is constrained to be a whole number, the second to a decimal with two digits past the decimal point, and the third is a decimal point with an indefinite number of digits.

At first glance, it may seem like DECIMAL and FLOAT are the same. But there is an important difference: DECIMAL stores a precise number with a specified number of decimal places, while FLOAT keeps an approximate value that may vary slightly by system. 

It’s important to use decimals when precision is necessary and only rely on floats if small rounding errors are acceptable.

Character and string data types

Character types store text. There are a few different types that differ in performance and storage efficiency.

• CHAR(n) is for fixed-length character sets. This is useful for data that always has the same size, like state codes or airport abbreviations.

• VARCHAR(n) is for variable-length character sets. It is useful for names, email addresses, and other user-entered text.

• TEXT and similar types are used for large blocks of unstructured text, like blog posts or product descriptions.

Some systems, like SQL Server, even support Unicode string types, such as NCHAR and NVARCHAR, which are useful when your data includes text in multiple writing systems, such as Latin, Chinese, or Cyrillic.

Let’s see what this looks like with an example table demonstrating different string data types:

CREATE TABLE customers (
    id INT,
    country_code CHAR(2),
    name VARCHAR(100),
    notes TEXT
);

INSERT INTO customers VALUES
    (1, 'US', 'Alice Johnson', 'Preferred contact by email.'),
    (2, 'CA', 'Bob Smith', 'Requires invoice copy for each order.');
    
SELECT * FROM customers;

Here’s what this table would end up looking like. You can see the length differences encoded in each string data type.

Here, CHAR(2) enforces consistent length, while VARCHAR and TEXT allow more flexibility.

Date and time data types

Date and time data types make it possible to store and query temporal information accurately. Beginners may think they can use numeric or string data types to encode this type of information. However, doing so can cause problems downstream with data analysis. Using a specific calendar data type can make everything smoother.

The three types most commonly used are:

• DATE, which encodes the year, month, and day,

• TIME, which stores the hour, minute, and second,

• and DATETIME or TIMESTAMP, which store a combined date and time.

Modern databases may even support fractional seconds or time zone–aware data types, such as TIMESTAMPTZ in PostgreSQL, which stores timestamps in UTC and automatically converts them to your session's time zone upon retrieval.

Let’s create a simple table to demonstrate these temporal data types:

CREATE TABLE events (
    event_id INT,
    event_date DATE,
    start_time TIME,
    created_at DATETIME
);

INSERT INTO events VALUES
    (1, '2025-11-26', '09:30:00', '2025-11-20 14:15:00'),
    (2, '2025-12-01', '18:45:30', '2025-11-21 09:00:00');
    
SELECT * FROM events;

Here’s what the tablewould look like:

You can see how choosing the right date or timestamp type can affect the type of data you can store.

Boolean and other special data types

Not all types of data can fit neatly into one of the previous categories. For these, SQL includes a set of special-purpose types for specific use cases. Some of the most common ones you might see include:

• Boolean values (BOOLEAN, BOOL, or BIT, depending on the system) for recording true or false,

• Binary data (BLOB) for images, files, or serialized objects,

• Large text (CLOB) for full-sized documents,

• JSON (in MySQL and PostgreSQL) for schema-less or nested data

• ARRAY (only in PostgreSQL) for lists

• UUID for unique identifiers that don’t rely on numeric sequences, like a session token or a global tracking code.

Special types let you store richer or more flexible data when traditional tables alone don’t capture the structure.

SQL Data Types Across Different Systems

Database management systems differ in the details of how they use these data types. While the broad categories stay the same, the exact names and behaviors vary across systems.

Different systems design their data types with their own priorities, which is why their behaviors differ. Some differences are small, like the exact keyword used. Others change how the data behaves in practice, such as limits on text length, how booleans are stored, or whether the system supports specialized formats.

MySQL emphasizes speed and simplicity, so it uses practical, lightweight choices like TINYINT (which is technically stored as numbers) for Boolean values. It favors performance and broad compatibility over strict type semantics. 

PostgreSQL aims to comply with standards and be extensible. This is reflected in the fact that it uses a true BOOLEAN type and has a richer set of data type options, such as ARRAY and JSONB for storing structured or nested data directly in a column. 

SQL Server is built for predictable performance in enterprise environments and deep integration with Microsoft’s ecosystem. This is why it offers strongly defined types like BIT for Booleans and VARCHAR(MAX) for large text. 

The table below gives a side-by-side look at how three major SQL systems implement common concepts.

What this means in practice

Each system’s underlying design goals and the kinds of workloads they are optimized to support directly influence the way they are built to operate. 

These differences mean that while many common SQL skills transfer easily between systems, details like maximum text size, auto-increment keywords, or support for complex structures can vary. 

If you are only using one system, it's easy to become an expert in the particulars of your database management system. However, changing jobs or departments may require you to jump into a different database management system with different rules. 

For this reason, when learning SQL, it's helpful to recognize the shared concepts behind the data types rather than simply relying on a single system’s particulars.

Choosing the Right Data Type

Selecting a data type is sometimes very straightforward. But sometimes it takes a bit of thought. A good choice makes your data easier to query, keeps storage lean, and helps avoid bugs downstream. The broad category will likely be determined by what kind of data you have. 

Numeric data

For example, suppose you are storing measurement data. Obviously, you're going to want some type of numeric, not a calendar or a string. But how precise are the measurements you want to store? Are integers OK, or do you need decimals? Do you want a fixed number of significant digits?

Then you should consider storage and efficiency. Choosing the smallest integer type that can safely hold your data will help to keep your tables efficient. Smaller types use less storage and can improve performance, especially in large tables. You'll usually want to pick a type large enough for your expected range without going far beyond what you’ll realistically need.

When an integer won't work, you have the choice of a DECIMAL or a FLOAT. Floating point types approximate values rather than storing them exactly, which can cause rounding errors in financial, scientific, or other accuracy-sensitive work. 

DECIMAL and NUMERIC store exact values, making them better for anything involving prices or ratios. However, DECIMALs tend to have larger storage requirements than FLOATs, depending on how many decimal points you save.

Character and string data

For string data, you'll want to think about how large you want each input to be. 

Usually, you'll want to size VARCHAR fields realistically rather than defaulting to the maximum length. Overly large limits don’t usually improve flexibility, but they can mislead future developers about the data’s expected size. 

Think about your expected inputs, whether that be names, emails, or product titles, and size the column accordingly.

Date and time data

For calendar data, it helps to be precise about what you’re storing. 

If you only need a date or only need a time, it’s usually better to use DATE or TIME instead of DATETIME. These choices make your intentions obvious and reduce the chances of mix-ups later. They also give the database a little more room to optimize date-only or time-only queries.

Boolean data

Whenever possible, use domain-appropriate types like BOOLEAN instead of workarounds such as integers or strings. Native types include built-in validation, clearer semantics, and support from database functions that expect specific kinds of data.

Below is a summary of common data storage goals and the recommended SQL data types to address them.

For readers who’d like a refresher on grouping and filtering data, the Intermediate SQL course walks through those core skills.

Common Pitfalls and Best Practices

Even a simple database schema can run into problems if data types aren’t chosen with care. 

Truncation

For example, if you have a column for names that only allows 5 characters, you may end up with “Micha” instead of “Michael”. This is a common problem known as truncation.

It typically shows up in fields like names, addresses, or comments, where real-world variation can exceed an optimistic limit. One way to avoid this is to size text fields based on real examples or err on the side of slightly too long.

Rounding errors

Another common issue is unexpected rounding when floating-point values are used for money or other precision-sensitive data. Because FLOATs store approximate values, small discrepancies can accumulate and affect reports, summaries, or calculations. 

You can prevent this by choosing DECIMAL for anything involving currency or exact decimal values. This will ensure the database keeps the numbers stable.

Type mismatches

Type mismatches can also lead to slow queries, especially in joins or comparisons.

When one column is stored as a string and another as an integer (or when each table uses a slightly different type), and you try to join them, the database may have to convert values on the fly. This can lead to errors and add unnecessary overhead. 

Keeping related columns aligned in type avoids these hidden conversions and helps queries run more smoothly.

Date formatting

Calendar dates have their own slew of problems. 

Date formatting inconsistencies are a leading source of confusion when systems mix local conventions or when developers store dates as strings rather than using proper date types. These inconsistencies can cause queries to misbehave or silently return incorrect results. 

The straightforward fix is to use actual date types and stick to a consistent, unambiguous format when working with dates in text form. I highly recommend our Time Series Analysis in SQL Server course to help avoid the most common calendar problems in your databases.

Best practices

Here are a few best practices to ensure your databases run smoothly:

• Populate tables with sample data early in the design process to reveal any weaknesses in sizing or typing choices.

• Document the purpose and expected ranges of each of your columns so future changes don’t introduce mismatches.

• Avoid using generic types when more specific ones would improve clarity.

Conclusion

Data types define how your database “understands” the values you store. They influence everything from storage to performance to clarity. Learning how they work makes your databases more reliable and predictable.

For further reading on this topic, check out SQL Database Overview Tutorial and SQLite Data Types: A Close Look at Storage Classes. I also recommend the PostgreSQL Summary Stats and Window Functions course, which shows how to compute running totals and ordered statistics.

A data type in SQL defines the kind of data a column can store and how that data behaves when queried or processed. It influences storage, validation, sorting, comparison, and mathematical operations.