Data warehousing: a governed single source of truth
Veratas designs and builds modern data warehouses on Microsoft Fabric, Azure Synapse, and Delta Lake. We bring the dimensional modelling discipline that turns scattered source systems into one trusted, query-ready model, so reporting stays consistent and the business stops arguing about whose numbers are right.
Why scattered source data fails the business
A warehouse is not a technical luxury. It is the answer to a recurring organisational failure.
Most organisations do not lack data. They lack agreement. Sales reports from the CRM, revenue from the ERP, and a finance spreadsheet all describe the same month and disagree, because each was built with its own filters, its own definition of an active customer, and its own cut-off date. Every leadership meeting then spends its first twenty minutes reconciling figures instead of deciding anything. That is the cost of having no governed model underneath the reports.
A data warehouse fixes this by separating the question of what the numbers mean from the question of where the numbers came from. Source systems keep doing their job: running transactions. The warehouse becomes the place where data is conformed, cleansed, and modelled once, so every report draws from the same definitions. Gross margin is calculated in one location, not redefined in fourteen dashboards. When the warehouse is built properly, disagreement about the numbers is replaced by discussion about the business.
The discipline that delivers this is dimensional modelling, not a pile of flat extract tables. Flat tables feel quick, but they drift: two teams export the same data on different days, apply different logic, and the estate fragments again. A governed warehouse with conformed dimensions and clearly defined fact grain holds its shape as it grows, which is precisely why we model before we build anything.
A data warehouse foundation, from $12,000
A fixed-scope data warehouse for one subject area, modelled and loaded. You see exactly what the price covers, and a fair quote for anything more.
Fixed fee from $12,000 | Live in 5 to 7 weeks | Price shown up front
A modelled, loaded data warehouse
One subject area, modelled as a star schema and loaded, ready for reporting.
- One subject-area star schema (facts and dimensions)
- ETL or ELT pipelines from your sources
- Built on Microsoft Fabric or Azure
- A semantic model for reporting
- Data quality and load validation
- Documentation and handover
Fabric capacity or Azure consumption is billed separately by Microsoft. The $12,000 covers the implementation services.
What is in the $12,000, and what we quote separately
Anything beyond the standard package is optional, and always quoted before you commit.
| In your $12,000 | Beyond the package, quoted at a fair rate |
|---|---|
| One subject-area star schema | An enterprise, multi-subject warehouse |
| ETL or ELT from your sources | Many sources and complex transformations |
| A semantic model | Real-time and streaming loads |
| Load validation | Master data management |
| Documentation and handover | Governance and a centre of excellence |
You pay $12,000 for the foundation. Everything else is optional, scoped and quoted transparently at a reasonable rate, and always shown before you decide.
Modern data warehouse delivery
We build warehouses that are governed, performant, and genuinely trusted by the people who report from them.
Platform and architecture
Warehouse design on Microsoft Fabric Warehouse and Lakehouse, Azure Synapse, or Delta Lake. We structure the estate as a medallion architecture with bronze, silver, and gold layers, so raw, cleansed, and business-ready data are clearly separated and lineage is obvious.
Dimensional modelling
Star-schema and dimensional design done properly: fact grain agreed up front, conformed dimensions shared across subject areas, and slowly-changing dimensions to preserve history. This is the modelling that keeps reporting consistent as scope expands.
ELT and transformation
Monitored ELT pipelines that land source data first, then transform it inside the warehouse where compute is cheap and logic is versioned. Every load is incremental where possible, with full data-lineage tracking from source column to gold model.
Performance and governance
Partitioning, statistics, and query tuning so reports return quickly under real load. Microsoft Purview cataloguing and row-level security give the business governed self-service without exposing data people should not see.
Medallion layers and the modelling that holds them together
The layered design is not jargon. It is what keeps a warehouse maintainable over years.
Medallion architecture organises the warehouse into three deliberate layers. Bronze holds raw source data, landed with minimal change, so you always have an untouched copy to reprocess from. Silver is cleansed and conformed: keys are standardised, data types are corrected, duplicates are resolved, and reference data is aligned. Gold holds the business-ready dimensional models that reports and semantic layers consume. Keeping these separate means a logic change is made once, in the right layer, and reprocessing is predictable rather than frightening.
Inside the gold layer, the choice of fact grain is the single most important decision. Grain is the level of detail one fact row represents: an order line, a daily account balance, a single shipment event. Pick it too coarse and questions become unanswerable; pick it inconsistently and facts cannot be combined. We agree grain in workshops, write it down, and design dimensions around it. Slowly-changing dimensions then decide how history behaves: type 2 versioning preserves what a customer’s region was at the time of sale, which matters the moment anyone reports a trend.
On Microsoft Fabric the warehouse and lakehouse sit over the same Delta Lake storage, in OneLake, which removes a long-standing source of friction. Engineers can work with files and Spark in the lakehouse while analysts query the warehouse with T-SQL, and both see one governed copy of the data. There is no nightly export from the data platform into the BI platform, because Power BI reads the gold model directly. Fewer copies means fewer chances for the numbers to drift apart.
Warehouse delivery, phase by phase
Data warehouse builds run through five phases, with validation built into each one rather than saved for the end.
Model
Source analysis, business-question gathering, and dimensional model design. Facts, dimensions, grain, and conformed reference data are agreed and documented before any pipeline is built, because rework here is expensive later.
Foundation
Platform setup on Fabric, Synapse, or Delta Lake. We establish the medallion layer structure, the security model, naming standards, and CI/CD pipelines so changes are deployed and tracked, not hand-applied.
Build
ELT pipeline development, transformation logic, and warehouse population, delivered in agile sprints. Each subject area is built, demonstrated, and reviewed with stakeholders so direction is corrected early.
Validate
Reconciliation against source systems, automated data-quality checks, and performance testing under realistic concurrent query load. Discrepancies are resolved before any report is published on the model.
Operate
Monitoring, incremental-load tuning, and ongoing model extension under managed services. New subject areas are added to the same governed foundation rather than spun up as separate silos.
Why clients choose Veratas for data warehousing
A warehouse is only useful if the business trusts it, so we build for trust as deliberately as we build for speed.
Modelling discipline
Proper dimensional modelling with conformed dimensions and agreed grain, not a collection of flat tables that drift apart. The model holds its shape as subject areas are added, which keeps long-term cost down.
Reconciled and trusted
Every load is reconciled against source systems with counts and control totals. The business sees figures that match what they already know, so adoption follows instead of being chased.
Built on Microsoft Fabric
Warehouses on Fabric and Delta Lake: a scalable foundation with Direct Lake reporting, OneLake storage, and unified Purview governance across engineering and BI.
ERP-aware
Deep experience modelling Dynamics 365 and enterprise ERP data. We understand how the source records transactions, so the warehouse reflects the business correctly rather than copying the schema blindly.
FAQ
Frequently asked questions
Quick answers to questions you may have. Can't find what you're looking for? Check out our full documentation.
Build a data warehouse the business will trust
A governed, well-modelled warehouse is the foundation for reliable reporting and analytics. Start with a conversation about your source systems and reporting goals.