The warehouse the rest of BI sits on.
Cloud data warehouse architecture, schema design, and the foundation that decides whether your BI practice compounds or collapses under its own weight as the business scales.
Built layer by layer.
A warehouse architected for decisions.
Most data infrastructure fails quietly. Queries slow. Costs balloon. Dashboards break when a source schema changes. Analysts spend more time debugging pipelines than answering questions. The root cause is almost always the initial architecture — decisions made when the data volume was smaller, the team was leaner, and the real reporting needs were still hypothetical.
We architect warehouses for the reporting and modelling workloads they will actually run. Schema design oriented around the questions the business needs answered, not around mirroring source systems. Partitioning and clustering decisions made for query patterns, not for default settings. Cost controls and governance frameworks baked in from the start so the warehouse scales without becoming a budget crisis.
The engagement typically covers cloud warehouse setup or migration, schema and data model design, cost optimisation, governance and access control, and the ingestion infrastructure that feeds it. Downstream workstreams — data engineering, visualisation, activation — all sit on this foundation.
What makes the difference.
Cloud Warehouse Architecture
Warehouse design calibrated for the workloads it will actually run. Schema, partitioning, clustering, and cost optimisation decisions made deliberately — not by default.
Data Model Design
Dimensional modelling, marts structured around real business questions, and semantic layer design that keeps reporting logic consistent across tools and teams.
Cost Governance
Query cost monitoring, quota management, and the budget guardrails that keep a growing warehouse from becoming a runaway expense. The operational discipline most teams learn the hard way.
Access Control & Security
Role-based access, row-level security where appropriate, audit logging, and compliance-aware design. Sensitive data treated as sensitive — not as a DDL afterthought.
Ingestion Infrastructure
ELT pipelines from every relevant source — ad platforms, analytics, CRM, commerce, finance — with incremental syncs, backfill capability, and the hygiene checks that prevent silent data corruption.
Migration Experience
Warehouse migrations are rarely clean. We have run them — from legacy analytics platforms, from other cloud warehouses, from home-grown analytics databases. The migration methodology is battle-tested.
Building the foundation.
Discovery
Source system mapping, reporting requirement analysis, current-state architecture review, and the future-state requirements that the warehouse needs to support.
Architecture
Warehouse, schema, and data model design documented. Partitioning, clustering, cost, and governance decisions made with explicit reasoning — not defaults.
Build
Warehouse stood up or migrated. Ingestion pipelines deployed. Access control configured. The foundation ready for downstream data engineering and reporting work.
Operate
Cost monitoring, performance tuning, and ongoing governance. Handoff to your data team with documentation and operational runbooks — or continued operations as part of the engagement.
Common questions.
Ready to build the foundation?
Let's talk about a warehouse architecture that supports the reporting and modelling workloads your business actually runs.
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