Service
Lakehouse and warehouse platforms designed for the workloads you actually have — and for the people who will run it after we leave.
Who it is for
Problems we solve
How we help
Typical deliverables
Architecture and design over four to eight weeks, or a design-and-build engagement delivered alongside your team.
Technology fit
We are independent: no reseller agreements and no partner quotas. If your estate points a different way, we will say so.
Governance & production readiness
A platform without governance is a liability with good query performance. Access, lineage and quality are designed alongside the architecture, not bolted on after go-live.
Example outcomes
Anonymised and sector-level. Client names are withheld by design — the constraint and the architecture are the parts that carry any information.
Enterprise
The constraint. Very large production tables with live dependencies, and no acceptable downtime window.
Migration designed and executed into Redshift, Snowflake and PostgreSQL using change-data-capture and a phased cutover, so the business kept reading from a consistent view throughout. The estate ended on a platform the in-house team could actually operate.
Regulated data
The constraint. Governance that had been written down but never enforced, across an estate holding personal data.
Metadata cataloguing, lineage, RBAC, retention and automated data quality checks with named owners and a remediation path — so that a governance claim became something a regulator could be shown rather than told.
Questions
A lakehouse puts warehouse-style tables and transactions on top of cheap object storage, so one platform serves both analytics and engineering workloads. You need one if you have significant unstructured or semi-structured data, or ML workloads alongside BI. If you have neither, a well-modelled warehouse is simpler and we will say so.
With change-data-capture and a phased cutover, so the old and new platforms run in parallel and the business keeps reading from a consistent view throughout. The hard part is never the small tables; it is the very large production ones with live dependencies, and those are planned first rather than last.
By modelling the cost at design time and attributing it afterwards. Warehouse sizing, storage layout, partitioning and job scheduling all move the bill more than most teams expect. Without attribution you cannot tie spend to a workload, and the only lever left is a blunt one.
A layering convention — raw, cleaned, and business-ready — that makes data quality and lineage explicit. It only works if each layer is a genuine contract rather than a folder name. Where it is only a naming scheme, consumers reach past it into raw data and you have the complexity without the benefit.
A first call is technical, not a sales call. Bring a problem, an architecture, or a stalled initiative.
30 minutes, no pitch deck.