Database Decision Framework
Choose your database based on data access patterns, not hype. If your data is under 10GB with any workload, PostgreSQL handles everything. Between 10GB and 1TB with complex queries, PostgreSQL with proper indexing and partitioning is still the answer. Need sub-second analytics on 1TB+ data, use ClickHouse (self-managed, fastest), BigQuery (serverless, GCP), or Snowflake (managed, multi-cloud). Need full-text search? Elasticsearch as a secondary index. Key-value at massive scale with known access patterns? DynamoDB. Caching and real-time features? Redis in front of your primary database.
Choosing a database is like choosing a vehicle: PostgreSQL is the minivan (does everything, fits everyone). Only get a race car (ClickHouse) if you are racing, a forklift (DynamoDB) if you are moving pallets, or a search helicopter (Elasticsearch) if you need to scan from above. Start with the minivan.
The decision matrix expands by use case: SaaS application backend with complex queries -> PostgreSQL. E-commerce product catalog with polymorphic products -> MongoDB or PostgreSQL with JSONB. Analytics dashboard with 1B+ events/day -> ClickHouse for real-time, Snowflake for BI. User sessions and rate limiting -> Redis. IoT device telemetry with millions of devices -> TimescaleDB or InfluxDB. Full-text product search -> Elasticsearch backed by PostgreSQL. Multi-player game leaderboard -> Redis sorted sets. Event sourcing -> PostgreSQL (simple) or Kafka + ClickHouse (scale). Recommendations engine -> PostgreSQL for small scale, Redis for serving, dedicated ML pipeline for computation. Always consider team expertise — a PostgreSQL expert team will build a better system with PostgreSQL than a team learning DynamoDB on the fly.
Advanced considerations: total cost of ownership goes beyond license/hosting. Include operational overhead (monitoring, backups, failover, upgrades, on-call), developer productivity (how fast can you iterate?), and hiring (can you hire engineers who know this technology?). Vendor lock-in: DynamoDB locks you into AWS, BigQuery locks you into GCP, Snowflake is multi-cloud. PostgreSQL, MySQL, ClickHouse, and Redis are open-source and portable. The polyglot persistence pattern (multiple databases for different workloads) is powerful but each database adds operational tax. My rule: start with PostgreSQL + Redis. Add ClickHouse or BigQuery when analytics needs exceed PostgreSQL capabilities. Add Elasticsearch when you need relevance-ranked search. Add DynamoDB only for specific high-throughput key-value workloads. Never add a database just because an article said it is better for some theoretical workload.
My decision framework is opinionated and practical. Default to PostgreSQL — it handles OLTP, moderate analytics, JSONB documents, full-text search, and geospatial queries in a single engine. Add Redis for caching, sessions, and real-time features. When analytics outgrows PostgreSQL, add ClickHouse for sub-second dashboards or Snowflake/BigQuery for enterprise BI. Add Elasticsearch when you need relevance-ranked text search. Add DynamoDB only for specific high-throughput key-value workloads where you have well-defined access patterns. Add MongoDB only when you have genuinely polymorphic data that does not fit a relational model. The key principle is minimizing database count: every database you add requires monitoring, backups, failover, version upgrades, and on-call expertise. Start simple, add complexity only when measured performance demands it.
Choosing a database because an influential blog post or conference talk recommended it for a workload you do not have. The right database is the simplest one that meets your actual requirements. PostgreSQL handles far more than most engineers realize — it should be your default until you have measured evidence that it cannot handle your specific workload.