Databases

Database decisions tend to outlast the team that made them, which is why the choice between engines and models deserves more than a quick default. This hub collects our engineering team’s writing on relational and non-relational systems, covering the tradeoffs behind sql vs nosql and the practical question of which types of databases fit a given workload, whether that’s transactional systems, analytics, or high-throughput web applications. If you’re weighing options for a new project, our current database rankings is a reasonable place to start, followed by a look at database picks for web apps once you know your access patterns.

A good share of the articles here focus on PostgreSQL and SQL Server, since most of our client work runs through one or the other, alongside deeper pieces on Cassandra, HBase, DuckDB and Snowflake for teams handling wider or more specialised data volumes. For readers still deciding between vendors, our comparison of Postgres and Oracle lays out the real differences in licensing, tooling and performance rather than just feature lists. We’ve also built out engagements where clients needed schema design, migration planning or query tuning handled by people who do it daily, which is where our SQL developers get involved directly in the build.

Some problems are less about which database and more about keeping the one you have running well. That’s covered too, alongside outage handling and performance work that comes up once a system is in production and under real load. When a project needs sustained SQL development services rather than a single audit, this is usually the point where teams bring in outside engineers. For the analytics side of the stack, the Business Intelligence and Analytics hub picks up where storage decisions leave off, and Data Engineering and Governance covers pipelines and data quality in more depth.