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SQL vs NoSQL Databases

Choosing between SQL vs NoSQL databases is one of the most common early architecture decisions. This guide explains what SQL and NoSQL databases are, how they differ, and when to use each, with practical examples you can run locally using Docker.

If you care about data integrity, scalability, and long-term maintainability, the choice matters.

What is a SQL database?​

A SQL (relational) database stores data in structured tables with a fixed schema (rows and columns). Relationships between data are expressed explicitly and queried using SQL.

Key properties:

  • Fixed schema with well-defined data types
  • Strong consistency and ACID transactions
  • Powerful joins and expressive queries
  • Mature tooling for migrations, backups, and analytics

Common SQL databases include PostgreSQL, MySQL, and SQLite.

SQL databases are a good fit when data correctness, transactions, and complex queries matter.

What is a NoSQL database?​

A NoSQL (non-relational) database stores data using flexible data models such as documents, key-value pairs, wide columns, or graphs.

Key properties:

  • Flexible or schema-less data models
  • Designed for horizontal scaling
  • Often prioritize availability and performance
  • Data is modeled around access patterns rather than relations

Common NoSQL databases include MongoDB (documents), Redis (key-value), and Cassandra (wide-column).

NoSQL databases are a good fit when schemas change frequently, access patterns are simple, or scale is critical.

SQL vs NoSQL: core differences​

FeatureSQLNoSQL
SchemaFixed, explicitFlexible or implicit
TransactionsStrong ACIDOften limited or optional
ConsistencyStrong consistencyEventual or tunable
JoinsNative supportUsually avoided
ScalingVertical + replicasHorizontal by design
Typical use casesBusiness data, finance, analyticsCaching, events, content
tip

The right choice depends on how your application reads and writes data, not on popularity or trends.

Practical example: modeling the same data​

Access pattern​

List incomplete tasks, filter by priority, occasionally query metadata.

SQL example (PostgreSQL)​

CREATE TABLE tasks (
  id SERIAL PRIMARY KEY,
  title TEXT NOT NULL,
  completed BOOLEAN DEFAULT FALSE,
  metadata JSONB,
  created_at TIMESTAMPTZ DEFAULT now()
);

INSERT INTO tasks (title, metadata) VALUES
  ('Write docs', '{"priority":"high"}'),
  ('Run tests', '{"priority":"low"}');

SELECT id, title
FROM tasks
WHERE completed = false
  AND metadata->>'priority' = 'high';

This works well because SQL handles filtering, indexing, and consistency naturally.

NoSQL example (MongoDB)​

db.tasks.insertMany([
  {
    title: "Write docs",
    completed: false,
    metadata: { priority: "high" },
    createdAt: new Date()
  },
  {
    title: "Run tests",
    completed: false,
    metadata: { priority: "low" },
    createdAt: new Date()
  }
]);

db.tasks.find(
  { completed: false, "metadata.priority": "high" },
  { title: 1 }
);

This is simpler to evolve but requires careful modeling as queries grow.

Running both locally with Docker​

services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_PASSWORD: example
      POSTGRES_DB: demo
    ports: ["5432:5432"]

  mongo:
    image: mongo:6
    ports: ["27017:27017"]
docker compose up -d
psql postgresql://postgres:example@localhost:5432/demo
mongo --host localhost

When to use SQL​

  • You need strong transactions (payments, inventory, auth)
  • Data has clear relationships
  • Reporting and analytics matter
  • Long-term data integrity is critical

When to use NoSQL​

  • Schema changes frequently
  • Access patterns are simple and predictable
  • You need fast key-value access or caching
  • Horizontal scaling is a priority

When not to use each​

  • Don’t use NoSQL to avoid learning SQL
  • Don’t use SQL for high-throughput event data without planning scaling
  • Don’t mix models without a clear reason

Common mistakes​

  • Assuming NoSQL is always faster
  • Skipping indexes until performance breaks
  • Modeling relational data in document databases without redesign
  • Ignoring backups and migrations
  • Using databases to store large binary files instead of volumes or object storage

Key takeaways​

  • SQL databases prioritize structure, consistency, and complex queries
  • NoSQL databases prioritize flexibility and horizontal scaling
  • Design from access patterns, not ideology
  • Indexes, backups, and migrations matter in both worlds
  • Prototype early with realistic data before committing

Next:

Next steps​