Databases • ~13 min read

SQL vs NoSQL Databases: Complete Comparison

Choosing between SQL and NoSQL databases is a fundamental architecture decision. This guide covers structure, scalability, consistency models, performance, and practical use cases to help you make the right choice.

Database paradigms overview

Databases fall into two broad categories: SQL (relational) and NoSQL (non-relational). SQL databases organize data into structured tables with predefined schemas, while NoSQL databases offer flexible, schema-less storage optimized for specific data models.

Neither is universally superior—the right choice depends on your data structure, access patterns, consistency requirements, and scale.

SQL databases

SQL (Structured Query Language) databases are relational: data is organized into tables (relations) with rows and columns. Relationships between tables are defined via foreign keys.

Key characteristics

Example schema

-- Users table
CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  created_at TIMESTAMP DEFAULT NOW()
);

-- Orders table
CREATE TABLE orders (
  id SERIAL PRIMARY KEY,
  user_id INTEGER REFERENCES users(id),
  total DECIMAL(10,2),
  status VARCHAR(50)
);

Advantages

Disadvantages

NoSQL databases

NoSQL databases abandon the relational model for flexible, schema-less storage. They're optimized for specific use cases: massive scale, high throughput, flexible data models, or distributed architectures.

NoSQL types

Document database example (MongoDB)

// User document
{
  "_id": "507f1f77bcf86cd799439011",
  "email": "user@example.com",
  "profile": {
    "name": "John Doe",
    "age": 30
  },
  "orders": [
    {
      "id": "ord_123",
      "total": 99.99,
      "items": ["item1", "item2"]
    }
  ],
  "created_at": "2025-01-15T10:30:00Z"
}

Advantages

Disadvantages

Key differences

AspectSQLNoSQL
Data modelTables with rows/columnsDocuments, key-value, graphs, etc.
SchemaFixed, predefinedFlexible, dynamic
ConsistencyACID (strong)BASE (eventual)
ScalabilityVertical (harder horizontal)Horizontal (distributed)
QueriesComplex SQL (JOINs, subqueries)Simple lookups, limited JOINs
RelationshipsForeign keys, normalizedEmbedded or duplicated data
Use caseStructured, relational dataUnstructured, high-scale, flexible

ACID vs BASE

SQL and NoSQL databases prioritize different consistency and availability trade-offs, formalized as ACID and BASE models.

ACID (SQL databases)

ACID prioritizes correctness and data integrity. Essential for financial systems, e-commerce checkouts, and any scenario where inconsistent data is unacceptable.

BASE (NoSQL databases)

BASE prioritizes availability and partition tolerance (CAP theorem). Acceptable for social media feeds, analytics, caching, and systems where eventual consistency is tolerable.

Scalability strategies

Vertical scaling (SQL)

Add more CPU, RAM, disk to a single server. Simple but has hard limits. Eventually you hit the ceiling of available hardware and cost-effectiveness.

Horizontal scaling

Distribute data across multiple servers. NoSQL databases are designed for this:

SQL databases can be sharded, but it's complex and loses many relational benefits (JOINs across shards are expensive). NoSQL databases handle sharding natively.

When to use each

Choose SQL when:

Examples:

Choose NoSQL when:

Examples:

Decision guide

Ask yourself these questions to choose the right database:

Data structure questions

Consistency questions

Scale questions

Query questions

Hybrid approach

Many systems use polyglot persistence: multiple database types for different purposes:

Don't feel locked into one choice—use the right tool for each job.