Data you can trust and use.

A database rarely fails dramatically on day one. It works fine with a hundred records, fine with a thousand, and then one ordinary afternoon, with real customers and real volume, a report times out, a page hangs, and an order goes missing. The problems were there all along; they just stayed quiet until the data grew enough to expose them.

Most of those problems trace back to decisions made early and never revisited: a structure that made sense for a prototype, an index that was never added, a backup nobody tested. We fix the foundation so the loud day never arrives, and if you're already living through it, we stabilise first, then fix the cause.

How you structure data determines how fast it is, how much it costs to run, and how painful every future feature will be. We design schemas that are normalised where it matters, denormalised where speed demands it, and honest about how your business relates its data, customers to orders, products to stock, payments to invoices.

Our default is PostgreSQL: powerful, reliable, free, and a sensible choice for the vast majority of business applications. For document-heavy or flexible-shape data we'll reach for MongoDB; for small, simple cases, MySQL or SQLite. We pick the tool that fits the problem, not the one we feel like using this month.

When something is slow, we don't guess, we measure. A performance audit surfaces the slow queries, the missing indexes, and the structural issues, and most databases can be made dramatically faster through the right indexes, query tuning, connection pooling, and caching, without a full rebuild.

Then there's the question that should keep every owner up at night: if this server died right now, how much would I lose? We set up automated backups, store them off-site, and, crucially, test them by actually restoring to a separate environment. A backup you've never restored is just a hope. We also lock down access, encrypt sensitive data at rest and in transit, and keep an audit trail of who touched what.

Moving databases (MySQL to PostgreSQL, MongoDB to SQL, an ageing legacy system to something modern) is where the fear lives, and rightly so. We treat it as a rehearsed operation, not a leap. We map the old data to the new structure, run the migration in a staging copy first, verify every record matches, and only then execute in production, usually with zero downtime.

There's always a rollback plan, so if anything looks wrong mid-flight we can step back without consequence. The aim is simple and absolute: every row that mattered before the migration still matters, and still exists, after it.