Design and optimize database systems that handle millions of transactions efficiently. From schema design to performance tuning, we ensure your data infrastructure is reliable, fast, and scalable for any workload.
A slow database is a slow product. Poor schema design compounds over time into queries that take seconds, migrations that require downtime, and a system that can't absorb growth. We fix that either by designing the right foundation from scratch or by diagnosing and resolving existing bottlenecks.
Our database engineers work across the full stack: relational databases (PostgreSQL, MySQL), document stores (MongoDB), key-value caches (Redis), search engines (Elasticsearch), and analytical warehouses (BigQuery, Snowflake). We choose the right tool for each access pattern not one database for everything.
For systems that need to be always-on, we implement active-active replication, automated failover, and backup strategies that meet even the most demanding RPO and RTO requirements.
From design to operation every aspect of your data infrastructure covered.
Normalised relational schemas and document models designed for your access patterns not just the data you have today but how it'll need to grow.
Query analysis, index strategy, execution plan optimisation, connection pooling, and caching layer design to eliminate slow queries at the root.
Dimensional modelling, ETL/ELT pipeline design, and analytical query optimisation for BigQuery, Snowflake, and Redshift data warehouse platforms.
Primary-replica replication, automatic failover, read replicas for scale-out, and multi-region configurations that survive infrastructure failures without downtime.
Automated backup schedules, point-in-time recovery, tested restore procedures, and disaster recovery runbooks for your defined RPO and RTO targets.
Schema migrations, data type conversions, cross-platform migrations (MySQL?PostgreSQL, Oracle?Cloud), and zero-downtime migration strategies.
Whether designing from scratch or optimising an existing system, we always start with understanding your data access patterns before touching anything.
We map every query type, access frequency, and data relationship before recommending a database technology or schema design.
Relational schema design, document model decisions, indexing strategy, and partitioning plan documented and reviewed before implementation.
Schema migrations with rollback capability, data seeding, query implementation, and integration with your ORM or data access layer.
Load testing, query profiling under realistic data volumes, and iterative optimisation until your performance targets are met.
Deep expertise across relational, document, graph, and analytical database systems.
Depends on your data model and query patterns. PostgreSQL is the better default for most applications it's relational, ACID-compliant, and more capable than most people realise with JSONB support. MongoDB suits genuinely document-oriented data with highly variable schemas. We help you make the right call before you're committed.
Yes. We start with an audit of your slow query log and EXPLAIN ANALYZE output to identify the biggest offenders. Most database performance problems come from missing indexes, N+1 query patterns, or inappropriate data types all fixable without a schema overhaul.
We use expand-contract (also called parallel-change) migrations: add the new column or table, write to both, backfill, then remove the old one. This works for most schema changes. For structural changes that require a full table rewrite, we use tools like pg_repack or online schema change tools to avoid table locks.
Share your performance challenges with us. We'll identify quick wins and a longer-term optimisation plan.