Fabric App availability in UK South
The Fabric region availability link says UK South now has Fabric Apps (it's not listed in the `Unavailable Fabric features` column). However, when I go to my UK South Fabric capacity, where quite a while ago I set "Enable Fabric App Items (preview)" to be "Enabled for the entire organization", Fabric Apps still don't appear in my UK South workspace when I want to add a New Item. Any idea why it's not appeared yet? https://learn.microsoft.com/en-us/fabric/admin/region-availability
Recovery retention policies differ across Development, Testing, Production, and Sandbox workspaces?
How should Item Recovery retention policies differ across Development, Testing, Production, and Sandbox workspaces? With Item Recovery available for supported Fabric items, should organizations apply the same recovery retention period across all workspace types, or should Production workspaces have a longer retention period than Development/Sandbox?
Monitor Hub search results paginated across multiple pages instead of returning consolidated results
The monitoring hub on the Fabric platform seems to be splitting search results, even when there are few, across multiple pages instead of condensing them into one page. Is this a known issue for others using the new Monitor Hub? Can this be rectified?
Fabric Capacity Permissions
So we have decided to move to Fabric F2 at my workplace but we're having issues with permissions. Me and my colleague cannot create workspaces and put it at Fabric Capacity, even though my boss has granted us Capacity Permissions. Our boss who is Capacity Admin can create those workspaces and make us Admin for those workspaces, but in those workspaces we cannot create SQL Databases or Warehouses. We had the same issue when were on the Trial-period. We cannot for the life of us figure out what to do. We want to be able to create Workspaces at Fabric Capacity and create all necessary artifacts such as SQL Databases and Warehouses.
Fabric Warehouse sync from Git Repo is failing (Unable to determine item status)
I am facing issue while trying sync warehouse changes from GIT to workspace. Noticed two things, first there is message next to warehouse name which says "Unable to determine item status" and second it allow "Accept incoming changes" only other option is disabled. After selecting Accept incoming changes then click on update all after some time it throw below error. Workload Error Code: DmsImportDatabaseException Workload Error Message Error occured during import database for the Datawarehouse '968570ea-b16e-49a6-8cf1-28eb56e4ba28@fd6ac02e-206d-4262-95f3-e70ca61b5792'. File: , Error: The database is currently locked by another operation. Please retry after the current operation completes. Apart from sync, tried following options as well. Pause / restart associated fabric capacity. Reverted existing workspace changes to bring warehouse code at same level as GIT repo Tried downloading fabric warehouse as SQL Database project but that also throws error Archive is undefine Observed errors messages during troubleshooting process - Archive is undefined, or ExportDefinitionFailed, or GitSyncFailed or InternalError or Unable to determine item statusArchitectural Advice: Isolating Client Reporting vs. ETL Workloads on F64 Capacity
Hello Fabric Community, We currently run both high-concurrency Client Portal Reports and Heavy ETL/Analytics (PySpark pipelines, lakehouse refreshes, ad-hoc queries) on a single F64 Capacity, leading to resource contention. Proposed Strategy: Separate Capacities: Split into two dedicated capacities—one strictly for user-facing client reporting and one for backend ETL/analytics. (Bonuse stratgey - not a primary focus )Database Mirroring: Mirror source databases directly into our Medallion Lakehouse to reduce pipeline batch ingestion load. Questions: Is splitting an F64 into two separate capacities the recommended way to protect client-facing reporting, or is workspace governance enough? What are the trade-offs regarding CU efficiency and cross-capacity OneLake shortcuts? Has database mirroring proven more CU-efficient than standard metadata-driven copy pipelines? Thanks for your insights!Cannot log ticket on Since 30 Sep update, Fabric Planning sheets go blank with any model measure.
Cannot log ticket on Since 30 Sep update, Fabric Planning sheets go blank with any model measure. Column sums still work. Planning sheets blank ("No rows to display") when using semantic model measures. Started after the 30 Sept service update.
Ms Fabric & PBI in different region
We are currently modernizing our Sales & Service KPI reporting platform using Microsoft Fabric and would appreciate advice from the community on the best way forward.Our source data is in an on-premises Oracle database, and we need to ingest Sales & Service KPI tables into Fabric for reporting. The main challenge is that our existing Power BI environment and Microsoft Fabric environment are hosted in different regions. Because of this: The existing On-Premises Data Gateway can currently be accessed only by Power BI artifacts such as Dataflows. Fabric artifacts such as Pipelines, Copy Activity, Mirroring, etc. are unable to use the existing gateway configuration. Microsoft documentation indicates that moving an existing Power BI tenant/workspace to another region is also not straightforward. Due to this limitation, we currently have access only to Dataflow Gen2 for data ingestion and need to proceed with the project using Dataflows initially. Some of the key design and implementation questions we are trying to address are: Should all tables be loaded through one Dataflow Gen2 or split into multiple Dataflows? What is the recommended approach for full load at time, full load on month ends & incremental loading: N-1 day append or watermark/Last Updated Date? What is the best approach to move from daily batch loads to near real-time reporting in the future? What are the current best practices for Lakehouse backup and disaster recovery? Has anyone faced a gateway region mismatch between Power BI and Fabric? If so, what was the long-term solution? For an F64 capacity, how do Dataflow Gen2 and Fabric Pipelines compare in terms of performance, scalability, and capacity consumption? understanding the capacity impact of using Dataflows as the primary ingestion mechanism. Any recommendations, best practices, or lessons learned from similar Fabric implementations would be greatly appreciated.FabCon & SQLCon 2026: The Ultimate Roundup of Announcements
While I wasn’t able to catch the action live in Barcelona last week for FabCon and SQLCon 2026, keeping tabs on the announcements has been a wild ride. Microsoft Fabric has officially cleared the 40,000-customer milestone and the event heavily emphasized Frontier Transformation – moving past standard AI productivity hacks and embedding autonomous agents directly into corporate workflows. Yet, among all the flashy Copilot integrations and agentic apps, one specific announcement completely stole the show for me: the introduction of the F0 capacity tier with on-demand billing. For anyone who has ever wrestled with budget approvals just to test out a new data platform, this is a game-changer. Let’s dive into why F0 is my favorite takeaway, alongside a full roundup of everything else that dropped in Barcelona. 1. My #1 Favorite: The F0 Capacity SKU & Pay-As-You-Go Billing Historically, dipping your toes into Microsoft Fabric meant committing upfront to a reserved compute tier (like an F2 or higher). If you just wanted to spin up a quick test environment, build a proof of concept, or play with OneLake shortcuts, you still had to navigate a monthly capacity bill. What’s new: Microsoft introduced F0, a zero-provisioned capacity SKU featuring true on-demand billing. Why it’s a massive deal: It acts as the “serverless” equivalent for Fabric. You can now evaluate features, run lightweight development tasks and test out workloads without locking into an upfront monthly reservation. Compute costs finally align strictly to what actually runs, shattering the barrier to entry for smaller teams and developer sandboxes. 2. Grounding Microsoft Copilot with Fabric IQ AI tools often hallucinate because they lack proper context. Microsoft is tackling this by wiring business context directly into Microsoft Copilot. What’s new: Fabric IQ is generally available as a shared intelligence bridge, connecting OneLake data, Power BI semantic models and operational metrics directly into Copilot Chat and Cowork – with zero additional AI token costs. Why it matters: Instead of guessing, your organization’s Copilot can answer business questions using the exact semantic data definitions your teams already trust and govern. Read more: Check out Arun Ulag’s keynote overview on the official Microsoft Azure Blog. 3. Power BI’s Evolution: Agentic App Creation Power BI is stepping past standard dashboards into operational application building. What’s new: You can use natural language inside Power BI Desktop to build, preview and publish purpose-built data applications straight from a trusted semantic model. These apps can handle inputs, write-back data and power workflows. Why it matters: Power BI Pro and Premium Per User (PPU) users will get access to Fabric Apps and Fabric Database capabilities (up to 1 GB per app) at no additional cost. Read more: Dive into Mohammad Ali’s deep dive on Power BI’s next chapter: The evolution of business intelligence. 4. Fast-Tracking Apps to Production with Fabric Apps & Rayfin Prototyping an app with AI is easy, but getting it to a secure production environment used to mean heavy lifting. Fabric Apps solves this. What’s new: Using the open-source Rayfin SDK and CLI, developers can build app backends and deploy them directly to Fabric with built-in TypeScript functions, a Secret Store, PostgreSQL support and private-by-default security. Why it matters: Apps plug straight into existing Lakehouses and Warehouses without data duplication. Read more: Catch Sachin Patney’s post, From prompt to production: What’s new in Fabric Apps. 5. Expanding the OneLake Ecosystem & IQ Sharing What’s new: Microsoft rolled out IQ sharing (in preview), allowing organizations to securely share governed data, markdown agent instructions and ontologies across different teams, partners and external ecosystems. Read more: Read Dipti Borkar’s breakdown on What’s new in Microsoft OneLake and its rapidly growing ecosystem. 6. Autonomous Data Engineering Agents What’s new: Driven by technology from the Osmos acquisition, Microsoft introduced a preview of the Fabric data engineering agent. Rather than just auto-completing code snippets, this agent handles complex, long-running engineering tasks like migrations, ETL pipeline creation and modernizations based on human guardrails. Read more: Check out Bogdan Crivat’s blog on Bringing governed analytics into the flow of work: Fabric Analytics at FabCon Europe 2026. 7. SQLCon: Scale, Intelligence, and Serverless Pauses Running parallel to FabCon, SQLCon delivered major updates for database administrators balancing traditional relational databases with modern demands. What’s new: A new Database Hub (Public Preview) serves as a single pane of glass to monitor health and security across SQL Server, Azure SQL and PostgreSQL. Plus, DiskANN vector indexes are now generally available to supercharge native semantic search using T-SQL. Read more: Read Shireesh Thota’s update on SQLCon Barcelona 2026: Advancing SQL with greater control, scale and intelligence. Final Thoughts: Looking Ahead Reflecting on everything announced in Barcelona, it’s clear that Microsoft is moving faster than ever to bridge the gap between heavy-duty data engineering and practical, AI-driven applications. While agentic workflows and Copilot enhancements grab the headlines, structural updates like the F0 tier are what truly make this ecosystem accessible to everyone from lone developers to enterprise architects. Honestly, I am still trying to digest all these announcements made and also can’t wait to try them soon! We are officially stepping into an era where building, scaling and operationalizing data isn’t just about managing tables – it’s about powering the intelligence layer that runs the business. Are you as thrilled about the F0 capacity tier as I am? What feature from FabCon are you testing first? Let me know in the comments below!
