Database

Databases are the backbone of most applications, storing and organising the data that powers systems and insights. Over the years, I’ve worked extensively with relational databases such as SQL Server, MySQL, and PostgreSQL, alongside document stores like MongoDB, graph databases such as Neo4j, and cloud-native solutions including Amazon DynamoDB.

PostgreSQL in particular has become a central part of my work. It is a powerful, open-source relational database known for reliability, extensibility, and strong standards compliance, with support for advanced data types and modern workloads.

I design schemas, optimise queries, and build data pipelines that support scalable applications, analytics, and machine learning systems. My experience spans transactional systems, analytical workloads, and high-availability cloud deployments.

PostgreSQL is often the default choice. I use it for traditional relational data as well as more advanced use cases such as geospatial analysis and AI applications. Extensions like PostGIS and pgvector allow geometry data, spatial queries, and machine learning embeddings to live alongside core business data in a single, coherent system.

A well-chosen and well-designed database can determine whether a system scales cleanly or struggles under growth. NoSQL solutions can be highly effective where flexibility or raw speed is required, but relational databases remain unmatched for structure, integrity, and consistency.

PostgreSQL strikes a rare balance between stability and innovation. Its ecosystem makes it possible to combine classic SQL reliability with modern capabilities in analytics and AI, reducing architectural complexity and keeping systems simpler, more durable, and easier to evolve over time.