Decision on using Fabric SQL or Fabric Warehouse
Hello, We are trying to migrate from synapse to fabric. Current solution is: source data= oracle OCI use Synapse Pipelines to copy oracle data to ADLS Gen 2. Synapse Dataflow to transform and destination is Azure SQL db. Serverless. We use this as DWH. a New client has more than 6TB of data. What data store we will use as the gold layer where we can build reports: fabric warehouse or fabric sql db since we are in Azure sql db? I yet to understand the hyperscale version and if this help chose fabric sql db? thanks
Export Power BI Report Page using Fabric Notebook
Hello Community, Is there a way to export a Power BI report page to PDF using Notebook while including all data contained within visuals that have scroll bar? I have Fabric Capacity and need to export a Power BI page to PDF while preserving the existing page layout and visual formatting. I can use Subscriptions, but the exported PDF only captures a snapshot of the data currently visible on the page. Some of my visuals contain vertical scroll bar, and any data that requires scrolling is not included in the PDF export. Has anyone found a way to export the full visual content, including the data beyond the visible scroll area, while retaining the report's formatting and layout using Notebook ?
Fabric capacity metrics vs activity logs for cost attribution
I have been working on the backend of a packaged Fabric Spend Analyzer, and the difficult part has been reconciling usage rather than building the dashboard. Capacity metrics, activity data, and workload-level signals can behave differently around spikes, background operations, and delayed telemetry. We added pipeline checks for missing or late data before it reaches the spend model, and tightened the DAX calculations around those cases. A few “correct-looking” numbers turned out to be wrong once we traced them back to the underlying usage data. For those building Fabric capacity reporting, how are you validating cost attribution when the underlying telemetry is incomplete or arrives at different times?
Fabric Data Agent visualization through MCP
I'm testing the new Fabric Data Agent visualization capability in the September 2026 update. In the native Fabric Data Agent experience, a question can return an interactive chart successfully. I'm consuming the same published Data Agent through its MCP endpoint from an external Next.js application. The MCP response gives me the answer/data, but I don't see the visualization specification needed for the external client to reproduce the chart. What is the supported way for an external MCP client to retrieve the visualization specification generated by the Data Agent and is this specification currently exposed through the MCP response/assistant run steps, or is visualization rendering currently limited to the native Data Agent experience?
Request for Free Microsoft Fabric Certification Exam Voucher
Hello Microsoft Fabric Team, I have completed all the modules and challenges required for the Fabric Data Days program, including the DP-700/DP-600 sessions. I am excited to apply my knowledge and would like to request the 100% discount exam voucher to schedule my certification exam. My Fabric Community username: KDMEENA Completed modules: All Fabric Data Days modules Thank you for providing this opportunity! I look forward to receiving the voucher and taking the exam. Best regards, Krishna Das MeenaSCADA and SAP silos to operational intelligence with Microsoft Fabric
One pattern I keep seeing in industrial analytics is that the hardest part isn't building another dashboard. It's getting production, maintenance, and financial data to align. In a recent engagement with an oil & gas client, my team at DataToBiz worked with an operator managing 6+ oil fields and 800+ production assets. Data was spread across SCADA, SAP, maintenance systems, and field spreadsheets. Production reporting could also lag by 24 to 48 hours. The architecture was built around Microsoft Fabric: Data Pipelines and Event Streams handled batch and near real-time ingestion from operational and enterprise sources. OneLake + Fabric Lakehouse provided the centralized data layer, with Bronze, Silver, and Gold Delta tables. Fabric Notebooks with Apache Spark handled transformation, cleansing, and KPI standardization. Power BI semantic models provided the governed business layer, with DAX measures and RLS for role-based access. Power BI dashboards brought production, maintenance, downtime, and executive views into a common reporting environment. Fabric Data Science with Python supported predictive maintenance and anomaly-detection use cases. The important design decision was separating ingestion, transformation, and the semantic layer instead of pushing all the logic into Power BI. That became particularly important for production KPIs. Different operational systems can represent the same metric differently, and simply centralizing the data doesn't solve that problem. The implementation automated 20+ recurring reports and enabled dashboards for 75+ business users across operations, engineering, finance, and leadership. For teams implementing Fabric in industrial environments, how are you handling KPI definitions across SCADA, ERP, maintenance, and field systems? Do you maintain the business definitions in metadata, enforce them in the transformation layer, or manage them primarily through the semantic model?
