SQL and NoSQL are not competing technologies — they solve different problems. This guide cuts through the marketing and gives you a clear decision framework based on your actual use case.
In this guide
SQL databases (PostgreSQL, MySQL, SQLite) store data in tables with rows and columns, enforce schemas, support ACID transactions, and use SQL for queries. NoSQL databases (MongoDB, DynamoDB, Redis, Cassandra) is an umbrella term for everything else — document stores, key-value stores, column stores, graph databases. They trade some relational features for flexibility, horizontal scaling, or specific access patterns.
Use SQL when: your data has relationships (users own orders, orders contain products). You need transactions across multiple tables. Your data model is relatively stable. You need complex reporting and aggregations. You are in finance, healthcare, or any compliance-sensitive domain. You have a small team — SQL is better documented, easier to debug, and more developers know it. The vast majority of web apps are better served by PostgreSQL than any NoSQL alternative.
Use NoSQL when: MongoDB for variable-structure documents (product catalogs, CMS content, user-generated forms with different fields per entry). Redis for caching, sessions, real-time leaderboards, and pub/sub messaging. DynamoDB for high-scale, single-table access patterns with massive throughput requirements. Cassandra or ScyllaDB for time-series data at extreme scale (IoT sensor data, analytics events). Pick the right tool for the specific problem.
A common misconception: "NoSQL scales horizontally, SQL does not." This is outdated. PostgreSQL scales vertically to 96-core machines and handles millions of queries per second. Read replicas handle read-heavy loads. Connection poolers handle thousands of concurrent connections. Tools like Citus make PostgreSQL horizontally sharded. Most applications will never need to scale beyond what a well-optimized PostgreSQL instance on a $400/month cloud server can handle.
For 95% of applications: start with PostgreSQL. It handles relational data, flexible document data (JSONB), full-text search, geospatial queries, and time-series data. Add Redis for caching and sessions. Only reach for MongoDB if your data is genuinely document-centric with highly variable schemas. Only reach for DynamoDB or Cassandra if you have validated you need millions of writes per second. Premature optimization of database architecture is one of the most common and expensive engineering mistakes.
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