Database design and data engineering
Data usually outlives the application built on top of it. We choose the database to fit the data and how it is queried, design the schema carefully, and plan backups, migrations and growth from the start.
The tools
Data models that stay fast and correct, on PostgreSQL, MongoDB, Supabase and Redis.
PostgreSQL
Our default relational database. Transactions, constraints and strong indexing keep data correct, and extensions such as pgvector add vector search for AI features.
MongoDB
A document database for flexible or deeply nested data, such as content, product catalogs and event logs, where the structure changes often.
Supabase
Hosted PostgreSQL with authentication, file storage, real-time updates and row-level security. A fast way to launch a first version without giving up a real SQL database.
Redis
In-memory storage for caching, sessions, rate limiting, queues and leaderboards, wherever answers must come back in milliseconds.
How we work with them
Schema design
Well-structured models, with keys and constraints enforced by the database itself, not only by application code.
Safe migrations
Every schema change is a versioned, reviewed migration script, tested against a copy of real data before it reaches production.
Performance tuning
We read query plans, add the right indexes and fix slow queries, and add caching only where measurements show it helps.
Backup and recovery
Automated backups, point-in-time recovery and regular restore tests, so you know a backup works before you need it.
Planning a project on this stack?
Tell us what you are building and we will tell you how we would approach it.