There's no universally 'better' database — only the right fit for your data and access patterns. Understanding the trade-offs between SQL and NoSQL lets you justify your choice, which is exactly what interviewers want to hear.
SQL (relational) databases
Data lives in tables with a fixed schema and relationships. They provide ACID guarantees (Atomicity, Consistency, Isolation, Durability) and powerful joins and queries. Great when data is structured and relationships and transactions matter — payments, orders, inventory. Examples: PostgreSQL, MySQL.
NoSQL databases
A family of non-relational stores optimised for scale and flexible schemas, usually favouring BASE (Basically Available, Soft state, Eventual consistency). Main types:
- Key-value (Redis, DynamoDB) — fastest lookups by key; caching, sessions.
- Document (MongoDB) — flexible JSON-like documents; content, catalogs.
- Wide-column (Cassandra) — huge write throughput; time-series, feeds.
- Graph (Neo4j) — relationships as first-class; social graphs, recommendations.
How to choose
Prefer SQL when you need strong consistency, complex queries/joins, and transactions. Prefer NoSQL when you need massive horizontal scale, very high write throughput, flexible/changing schemas, or a specific access pattern (key lookups, graph traversals). Many real systems use both — SQL for core transactional data, NoSQL for scale-out workloads.
Scaling considerations
SQL databases traditionally scale up and are harder to shard; NoSQL stores are usually built to scale out across nodes from day one. See sharding & replication and the CAP theorem for the deeper trade-offs.