Database Selection Guide
Choosing the right database is one of those decisions that's easy to get wrong early and expensive to fix later. This guide gives you a decision flowchart plus a breakdown of each database family so you can pick with confidence.
Quick Decision Flowchart
Start at the top and follow the path that matches your primary need.
Rule of thumb
If you're still unsure, default to PostgreSQL. It handles relational data well and has solid extensions for JSON, full-text search, and even vector search — so you can outgrow it slowly instead of migrating early.
Comparison at a Glance
| Type | Examples | Best For | Main Trade-off |
|---|---|---|---|
| Relational (SQL) | PostgreSQL, MySQL, SQLite | Financial ledgers, e-commerce, reporting | Rigid schema, harder to scale horizontally |
| Document (NoSQL) | MongoDB, Firestore, Couchbase | Prototyping, user profiles, CMS | Weak multi-record relationships/aggregation |
| In-Memory (Key-Value) | Redis, Memcached | Caching, sessions, leaderboards | Volatile, limited by RAM |
| Graph | Neo4j, Amazon Neptune | Social networks, fraud detection | Steep learning curve (Cypher, etc.) |
| Vector | Pinecone, Milvus | AI/LLM embeddings | Niche use case, extra infra |
| Time-Series | InfluxDB, TimescaleDB | IoT, metrics, stock ticks | Niche use case, specialized queries |
| Wide-Column | Cassandra, ScyllaDB | Massive global write volume | Operationally complex |
1. Relational (SQL)
- Examples: PostgreSQL, MySQL, SQLite, Firebase SQL Connect
- Data Structure: Rigid tables with rows, columns, and foreign keys.
- Best For: Financial ledgers, e-commerce, complex reporting.
- Pros: Bulletproof data integrity, powerful multi-table JOIN queries.
- Cons: Harder to scale horizontally, requires strict schema migrations.
2. Document (NoSQL)
- Examples: MongoDB, Firebase Firestore, Couchbase
- Data Structure: Flexible, schemaless JSON-like documents.
- Best For: Rapid prototyping, user profiles, content management.
- Pros: Easy to change data structures, scales out automatically.
- Cons: Poor support for complex relationship queries and data aggregations.
3. In-Memory (Key-Value)
- Examples: Redis, Memcached
- Data Structure: Ultra-fast key-value pairs stored in RAM.
- Best For: User session caching, real-time leaderboards, speed optimization.
- Pros: Sub-millisecond response times.
- Cons: Volatile data storage if not backed up; restricted by RAM size.
4. Graph Databases
- Examples: Neo4j, Amazon Neptune
- Data Structure: Interconnected "Nodes" (entities) and "Edges" (relationships).
- Best For: Social networks, fraud detection, recommendation engines.
- Pros: Fast querying of deeply nested relationships without slow JOIN operations.
- Cons: High learning curve for graph query languages (like Cypher).
5. Niche / Specialized
- Vector (Pinecone, Milvus): Stores AI and LLM data embeddings.
- Time-Series (InfluxDB, TimescaleDB): Logs high-frequency data like IoT sensors and stock ticks.
- Wide-Column (Cassandra, ScyllaDB): Handles massive global write volumes across multiple data centers.