Database Architecture & Performance

Database Technologies in 2025

Modern applications need more than one database. You might choose PostgreSQL 18.1 or MySQL 9.5 for relational data. Both give you ACID compliance, window functions and parallel query execution. Then add Redis 7.4 for sub-millisecond caching with multi-threaded processing. Elasticsearch 9.2 handles full-text search at scale. And if you need flexible schemas, MongoDB 8.2 offers document storage that adapts as your schema changes.

Different data problems need different solutions. Put transactional data with complex relationships in PostgreSQL. MySQL's excellent for high-volume reads. Need caching and real-time features? Use Redis. Full-text search requires Elasticsearch. Flexible schemas fit MongoDB. The point is polyglot persistence. Combine technologies based on what each part of your application actually needs, rather than forcing everything into one database.

Polyglot Persistence Architecture

We build database systems that scale from thousands to millions of queries per day. How? We combine relational databases for transactional data, Redis for caching and sessions, Elasticsearch for search, and specialised stores for time-series or documents. Each tool does what it's best at.

Database performance comes from proper design, not bigger hardware. Correct indexing is fundamental to query performance. Strategic caching reduces database load. Use 3NF normalisation for transactional tables. Denormalise selectively for analytics. This gives you both data integrity and query performance.

Multi-database architectures are complex to run. Connection pooling stops you burning through resources. Replication gives you reliability and the ability to read-scale. You need to monitor cache hit ratios, query performance, and connections in use. When you migrate between systems, logical replication and binlog approaches keep downtime minimal.

Production databases need expertise tested in production. Get the indexing wrong, skimp on connection pooling, or pick the wrong technology, and costs spiral as data grows. We design for scale from the start: proper normalisation, smart indexing, query pattern analysis, and choosing tools that match what you actually need.