Understand the fundamental structure of databases, including tables, indexes, and schemas.
We use databases every day—from websites and mobile apps to dashboards and APIs—but have you ever wondered how they actually store data under the hood?
Understanding how databases store, retrieve, and organize data gives you a deeper appreciation for performance, indexing, and schema design. Whether you’re new to SQL or building your first production app, this guide will help you grasp the fundamentals of data storage.
You can explore SQL structure hands-on in the Galaxy SQL Editor.
At the simplest level, a database is a structured collection of data. It stores data in tables (similar to spreadsheets) and allows efficient access, updates, and queries.
Most modern databases are relational—they organize data into related tables using rows and columns.
A table is the core data structure in SQL databases. Each table consists of:
For example, a users table might have columns like id, name, email, and created_at, with each row representing one user.
You can define tables with clear types using commands like:
CREATE TABLE users (
id SERIAL PRIMARY KEY,
name VARCHAR(100),
email VARCHAR(255),
created_at TIMESTAMP
);
Learn more in our SQL Data Types guide.
Behind the scenes, relational databases store data on disk using pages, blocks, and files. Here’s what that typically looks like:
Most databases use the file system to store data in a binary format. Each table corresponds to a file or set of files.
Data is grouped into fixed-size blocks (e.g., 8 KB pages in PostgreSQL). These pages are the smallest units of I/O.
By default, rows are stored in arbitrary order within data blocks—this is called a heap. When you query the database, it scans or indexes these blocks to retrieve rows.
Indexes are additional data structures that help speed up reads by avoiding full table scans.
For example:
CREATE INDEX idx_email ON users(email);
This creates a B-tree index to make queries like WHERE email = ... faster.
Indexes are critical for performance—especially with large datasets. Learn more about sorting and pagination with our LIMIT + ORDER BY guide.
When you write a query like:
SELECT * FROM users WHERE email = 'jane@example.com';
The database:
You can inspect query plans with tools like EXPLAIN in PostgreSQL or MySQL.
For every UPDATE, INSERT, or DELETE, the database:
This ensures atomicity, consistency, isolation, and durability (the ACID properties of transactional databases).
Most databases use a transaction log to record changes before they’re written to disk. This provides:
Traditional SQL databases like PostgreSQL and MySQL are row-oriented—they store complete rows together. This is good for transactional operations.
Analytics databases like ClickHouse, BigQuery, or Amazon Redshift use columnar storage, which stores values column-by-column. This speeds up queries that scan large amounts of data but only need a few columns.
Want to explore column vs. row layout? Try experimenting with queries in Galaxy’s SQL Editor.
Understanding how data is stored in a database helps you write better schemas, optimize performance, and debug issues faster. You don’t need to know every byte-level detail—but knowing the concepts behind tables, indexes, blocks, and storage models will make you a more confident developer or analyst.
To try real queries and see how data behaves in practice, visit the Galaxy SQL Editor.
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