What is the difference between SQL and NoSQL databases?

SQL and NoSQL databases take fundamentally different approaches to storing and querying data.

SQL (Relational databases)

Data is stored in tables with rows and columns. The schema is defined upfront and enforced — every row must conform to the table structure. Relationships between tables are expressed with foreign keys and joined at query time.

Examples: PostgreSQL, MySQL, SQLite, SQL Server.

Strengths:

• ACID transactions — atomicity, consistency, isolation, durability
• Powerful joins and aggregations with a standardized query language
• Well-suited when data has a clear, stable structure and relationships matter

NoSQL (Non-relational databases)

"NoSQL" covers several different storage models:

• Document stores (MongoDB, Firestore) — store JSON-like documents, flexible schema
• Key-value stores (Redis, DynamoDB) — fast lookup by key, great for caching and sessions
• Wide-column stores (Cassandra) — optimized for high write throughput and time-series data
• Graph databases (Neo4j) — nodes and edges, optimized for relationship queries

Strengths:

• Flexible or schema-less — easy to evolve data shape over time
• Horizontal scaling is simpler for most NoSQL types
• High write throughput in specialized engines

Choosing between them:

Use SQL when:

• Data is structured and relational (orders → customers → products)
• You need strong consistency and complex transactions
• You have reporting or analytics requirements

Use NoSQL when:

• Schema changes frequently or is unknown upfront
• You need high write throughput at scale
• Your data model maps naturally to documents, key-value pairs, or graphs

Most modern applications use both — a relational DB for core entities and a NoSQL store for caching, logs, or search.