Database Types: Choosing the Right Database for Your Use Case
There is no single "best" database — different database types are optimised for different use cases. Choosing the wrong database for a workload can result in performance problems, high costs, or complex workarounds. This article summarises the main database categories and when to use each.
Relational Databases (SQL)
Relational databases store data in tables with defined schemas, use SQL for querying, and enforce ACID transactions. Best for: structured data with clear relationships, transactional workloads (orders, accounts, inventory), applications that need complex joins and referential integrity. Examples: PostgreSQL, MySQL, SQL Server, SQLite.
Document Databases (NoSQL)
Document databases store data as JSON-like documents without a fixed schema. Best for: content management, product catalogues, user profiles — data with variable structure or nested objects. Examples: MongoDB, CouchDB, Firestore.
Key-Value Stores
Extremely fast databases optimised for simple get/set operations. Best for: caching, session storage, feature flags, rate limiting. Examples: Redis, DynamoDB (also supports complex queries).
Time-Series Databases
Optimised for data points indexed by time. Best for: monitoring metrics, IoT sensor data, financial tick data. Examples: InfluxDB, TimescaleDB (PostgreSQL extension), Prometheus.
Graph Databases
Optimised for highly connected data with complex relationship traversal. Best for: social networks, recommendation engines, fraud detection, knowledge graphs. Examples: Neo4j, Amazon Neptune.
Search Engines
Optimised for full-text search and faceted navigation. Best for: site search, product discovery, log analysis. Examples: Elasticsearch, OpenSearch, Typesense, Algolia.