05FinOps on Fabric — FOCUS-Standard Cost Reporting and Reservation Analytics
FOCUS cost intelligence in Power BI — 78% reservation coverage exposed for action
Role: Solution Engineer / FinOps
Executive summary
Built a Fabric-based FinOps solution that ingests Azure cost data, models it to the FOCUS standard, and surfaces reservation coverage and forecasts in Power BI—driving smarter reservation purchases.
- Microsoft Fabric
- Lakehouse
- Warehouse
- Dataflows
- Spark
- Power BI
- Azure Cost Management
- FOCUS
A large financial institution needed better cloud-cost visibility for Azure workloads, including reserved instances, with projections. They wanted a FinOps dashboard built on Microsoft Fabric and presented in Power BI following the FOCUS reporting standard.
- Ingest Azure Cost Management data into Fabric Data Engineering (Spark) and transform daily cost records.
- Structure data to FOCUS guidelines—by resource, tag, environment, reserved vs on-demand.
- Build Power BI reports for usage vs budget, reservation savings and underutilization.
- Enable near-real-time updates and drill-down by department/service.
- Embed FinOps best practices, including idle-resource recommendations.
Using Fabric Dataflows and Spark notebooks, I built ETL into a Lakehouse, aggregated by day/subscription/resource group, and enriched with reservation lookups to quantify savings. Results loaded into a Fabric Warehouse for Power BI. The dashboard featured summary cards (total spend, reservation savings, forecast vs budget) and interactive breakdowns, including reservation vs pay-as-you-go coverage. I kept the model on the FOCUS schema rather than a bespoke cost model so the client could later plug in multi-cloud data without rewrites, and I worked with the client's finance and ops teams to validate requirements and the coverage reading.
The dashboard gave granular Azure cost insight entirely within Fabric. The coverage view measured reservations against compute hours and surfaced the result—at 78% of compute hours covered—as a decision input for the next quarter's purchase plan rather than a vanity metric — the client could see where coverage was thin and what pay-as-you-go spend was exposed. The reading was anchored to external reference points rather than judgment alone — published FinOps KPI guidance puts RI/Savings-Plan coverage in a 70–80% band (so 78% was within industry range, with the remaining ~22% quantifying the open purchase window) and pairs coverage with a 95%+ utilization watch-out for over-commitment, which framed how the next purchase could grow coverage without waste. Daily automated refresh cut manual effort, and the FOCUS-standard executive report let leadership gauge efficiency and spot anomalies, strengthening confidence in Fabric beyond data science.
Fabric's scalable Spark engine handled growing cost-data volumes easily. Pairing FinOps expertise with implementation matters—context like reservation coverage only drives action when it is framed as a gap to close, not a score to celebrate — the same 78% number read wrongly would have ended the conversation instead of starting the purchase plan. Fabric/Power BI integration made collaboration and sharing seamless.