A warehouse that scales with you, not ahead of you
Warehouse and lakehouse architecture on Snowflake, BigQuery or Redshift. Sized for the volume you have now, structured so it doesn't need rebuilding at ten times that.
Any of these sound familiar?
"Our production database is also our reporting database"
Analytics queries compete with live traffic, and both get slower at exactly the wrong moment.
"Cloud spend went up and nobody knows why"
No warehouse sizing, no query monitoring, no way to attribute cost to a team or a workload.
"Everyone has admin"
Access is all-or-nothing, which becomes a real problem the first time you handle customer data.
"We're on spreadsheets and it's breaking"
Volume has outgrown the tooling, and every month-end close is a manual reconciliation.
A platform, not just a database
Warehouse or lakehouse setup
Sized, configured and cost-controlled on the platform that fits your workload and budget.
Layered storage design
Raw, staging and curated layers, so reprocessing history never means re-ingesting it.
Migration
Moving off legacy databases, spreadsheets or an older warehouse, running both in parallel until the numbers reconcile.
Access control and governance
Role-based access, row-level security where it's needed, and an audit trail that satisfies a reviewer.
Cost management
Auto-suspend, query monitoring and spend attribution, so the monthly bill is explainable line by line.
Infrastructure as code
The whole platform reproducible from a repository, rather than clicked together in a console nobody documented.
Tools we reach for
Platform choice depends on your volume, your team's existing skills and where the rest of your infrastructure already lives.
A typical engagement
- Weeks 1–2
Architecture
Platform selection, data model and cost modelling, with the tradeoffs written down.
- Weeks 3–5
Build
Environments, storage layers, access control and infrastructure as code.
- Weeks 6–10
Migrate
Source by source, with a parallel run and full reconciliation before anything is switched off.