Database Architecture

Slow products are usually slow at the database, and the fix is rarely "more hardware". We start with the schema and the query plans: proper normalisation, indexing strategies matched to your read patterns, and explain-plan analysis to find where the time actually goes.

For growing systems we implement the plumbing that keeps performance flat as data grows: connection pooling, Redis caching layers, and read replicas. We also run migrations and version upgrades with zero downtime.

We work across PostgreSQL and Amazon RDS for relational workloads, MongoDB and DynamoDB where document or key-value models fit better, and Redis for caching and real-time features.