A well-designed data warehouse is the backbone of every analytics and BI operation. We design, build, and optimise modern cloud data warehouses on Snowflake, BigQuery, and Redshift — with dimensional modelling, dbt transformations, and governance that ensures every report tells the same story.
Discuss Your ProjectA single, agreed semantic layer that eliminates conflicting numbers between teams.
Clustering, partitioning, and materialisation strategies for sub-second dashboard queries.
Cloud warehouses that scale compute and storage independently — pay only for what you use.
Business requirements translated into dimensional models and entity relationships.
Warehouse provisioning, dbt project setup, and initial load pipelines.
Data reconciliation against source systems and business user sign-off.
Access controls, cost alerts, and ongoing model maintenance.
Architectural BIM, scan-to-BIM, 3D visualisation, and automation — all under one roof.
Common questions about our Data Warehouse service.
BigQuery suits Google Cloud shops and workloads with unpredictable query volumes since it charges per query. Snowflake offers better multi-cloud flexibility, workload isolation, and fine-grained compute control. We recommend based on your existing cloud footprint and usage patterns.
Yes — we have migrated from Oracle, Teradata, Netezza, and MSSQL to modern cloud platforms. Migrations include automated schema conversion, data validation against the source, and a parallel-run period before cutover.
Dimensional modelling organises data into facts (measurable events) and dimensions (context like customer, product, or date). It makes business queries intuitive and fast. Most analytical warehouses benefit from at least a partial dimensional model.
We implement cluster and query scheduling, materialisation strategies that avoid repetitive full-table scans, credit usage alerts, and warehouse auto-suspend. Cost governance is part of every warehouse engagement, not an afterthought.
dbt sits between your raw data layer and your BI tool, running tested SQL transformations inside the warehouse. It version-controls business logic, generates a data lineage graph, and auto-documents every model. It is the transformation standard for Snowflake and BigQuery.
A focused implementation covering 3–5 data domains — for example sales, marketing, and operations — typically takes 8–14 weeks from kickoff to BI-ready data.
A semantic layer defines business metrics — revenue, churn rate, CAC — in one place so every BI tool and analyst uses the same calculation. It eliminates the "why are the numbers different" argument between teams.
Our team will scope your requirements and come back with a clear proposal within 48 hours.