Case study 06 · Microsoft data platform
Dataverse to Microsoft Fabric Platform for Project Operations & Sales
Replacing direct Dataverse reporting with an incremental, historised Fabric platform optimised for Power BI.
The challenge
Direct Power BI access to Dataverse limited report performance and created unnecessary load on operational sources. The reporting estate also needed reliable historisation so project, sales and financial changes could be analysed over time rather than only as a current-state snapshot.
Performance goal
Move analytical workloads away from direct Dataverse access and onto an optimised Fabric data layer.
History goal
Introduce reusable incremental-loading and SCD Type 2 patterns so business users can analyse how key entities change over time.
What was delivered
- A Fabric medallion platform for Project Operations and Sales reporting.
- Incremental pipelines and notebooks to process Dataverse entities while reducing source-system load.
- Watermark-driven orchestration and repeatable load patterns for reliable refreshes.
- SCD Type 2 historisation across Projects, Accounts, Employees, Tasks, Teams, Risks and Role Pricing.
- A Gold Warehouse layer with dynamic stored procedures orchestrated through scheduled pipelines.
- A business-ready consumption layer for faster, more reliable Power BI reporting.
Business value
- Improved report performance by moving Power BI off direct Dataverse access.
- Increased reliability and auditability through incremental loads and automated orchestration.
- Unlocked historical analysis of project, sales and financial data.
- Created reusable engineering patterns that can support additional Dataverse entities and reports.
Technology
- Microsoft Fabric
- Dataverse
- Lakehouse
- Warehouse
- PySpark
- Power BI
Commercial takeaway
For organisations already invested in Microsoft business applications, this turns Dataverse into a scalable analytics source without burdening the operational systems underneath it.
Related case studies
Reporting straight off your operational system?
We move analytical load onto a Fabric layer built for it, and add the history your current-state sources cannot keep.