Currently, when a button is set to the "Data function" action type, the Format/Property pane does not display which specific function is selected. To verify the logic, users have to click through multiple sub-menus, which is inefficient for debugging and auditing complex reports. I suggest displaying the Function Name (and its Function Set) directly in the top-level Action card. This would provide immediate clarity on what a button triggers without unnecessary "click-drilling," making it much easier to maintain data integrity and peer-review reports in a professional environment. ... View more

With matrix visuals, it would be helpful to have an option to show a value as the % of parent row, as you can with excel pivot tables.  ... View more

Add a visible property to all visuals that can accept either 0 (hide) or 1 (show).  This value can also be an expression/measure.  The filter pane currently does not support this. ... View more

Idea / Problem As Fabric pipelines evolve “beyond ETL” into long‑running, human‑aware workflows (per the recent blog post “Pipelines are evolving beyond ETL”), the current connection model is becoming a serious operational risk in real data engineering environments. Many pipeline activities now require Fabric connections that are: Identity‑bound and opaque Not discoverable by the rest of the team Not traceable to the pipelines that use them Not safely editable or replaceable by another engineer This creates hidden single points of failure. If the original author is unavailable (holiday, sickness, turnover), production pipelines can become unfixable. This might be acceptable for individual or business‑user scenarios, but it is not safe for team‑owned, production‑grade data engineering. Why this matters In professional data engineering: Pipelines are team‑owned assets Any engineer must be able to support failures Ownership cannot be implicit or personal Governance and auditability are mandatory Fabric currently conflates data‑plane connections (e.g. Copy sources/destinations, which work well) with delegated/action connections (e.g. Approval, Invoke Pipeline), even though they have very different operational requirements. Requested improvements At minimum, Fabric needs: Workspace‑ or team‑owned connections (not tied to individuals) Ability to see where a connection is used Safe rotation, replacement, and deletion Clear distinction between data‑plane and delegated/action connections Support for service principals / managed identities for workflow actions (e.g. Approvals) Without this, teams will be forced to avoid newer pipeline capabilities in production, despite their potential value.   Expected benefits Addressing this would: Make Fabric pipelines safe to operate as team‑owned production assets Reduce hidden single points of failure caused by person‑bound connections Improve supportability during incidents, on‑call, and staff absence Enable broader use of newer pipeline capabilities without increasing operational risk Align Fabric more closely with enterprise data engineering, governance, and audit expectations This would allow Fabric to evolve beyond ETL without compromising the safety and reliability that professional data engineering environments require. ... View more

Can storage location be added to the Inventory Componet report.  I have a site, 1626, with outside storage, I want to pull the inventory at both locations vs. showing it as a total. ... View more

 Recommend adding an API call for dataflows that provides detailed refresh information at a granular level for all items within the dataflow. Currently, the Get Dataflow Transactions API is very barebones and does not provide detailed insight into what specifically failed within the dataflow.   It would be beneficial for the new detailed-level API to be similar to the Get Refresh Execution Details In Group endpoint. The monitoring and manual extraction a Dataflows refresh is not scalable for logging analysis. We also do not want to enable farbic logging for workspace as that will create to much noise and throttle our capacity. The API is the easiest way we can scale what we want to do.    ... View more

Buenos dias   Una funcionalidad o caracteristica que creo seria util es que cuando interactuan 2 "Button Slicer"  y uno de ellos es agrupacion del otro, cuando filtras en la agupacion pero esta seleccionado un valor perteneciente a otra, se queda marcada y es necesario seleccionar una de las opciones que se muestran. Seria interesante, creo, que cuando se selecciona una agrupacion, se pueda decidir que se marque una opcion del otro, por defecto, no quedando la pantalla en blanco y sin datos por haber incongruencia entre ambos slicers. Pongo un ejemplo: Button  Slicer 1:  Empresa:  A Y B Button  Slicer 2:  Sede 1, Sede 2, Sede 3 y Sede 4 Empresa A: Sede 1 y Sede 2 Empresa B: Sede 3 y Sede 4 Si tenemos seleccionada la empresa B, en el slicer 1, tenemos sus 2 opciones en el slicer 2 y la sede 3 seleccionada, por ejemplo. Si ahora seleccionamos la empresa A, cambia los valores mostrados en el slicer 2, apareceran Sede 1 y Sede 2, pero sigue seleccionado sede 3, por lo que no se muestran datos en los visuales que dependan de ellos.   Creo que el hecho de que al seleccionar el Slicer 1 marque por defecto una de las opciones, por ejemplo la primera, aportaria una mejora en la experiencia del usuario   Espero haberme explicado con claridad   Gracias           ... View more

It would increase the efficiency of my work if i could copy paste filters when developing a PBI report. There are times (not rare) where you change your mind on where the filter should be or maybe you want a specific filter on several visualizations, and some of those filters can be complex and a pain to duplicate. ... View more

Current RAG implementations in Microsoft Fabric (via AI Skills, Azure AI Search integration, and OneLake-backed vector stores) rely predominantly on vector similarity search over chunked documents. While this works well for surface-level semantic retrieval, it has significant limitations for enterprise scenarios that require multi-hop reasoning across structured and unstructured data: Loss of relational context: When documents are chunked and embedded, the relationships between entities (customers, products, transactions, regulations, contracts) are lost. Vector search retrieves semantically similar chunks but cannot traverse "Customer A → owns → Account B → linked to → Transaction C → flagged by → Regulation D."   What Would Help A first-class Graph-Based RAG capability natively integrated into Fabric, including: OneLake Graph artifact — a new item type (alongside Lakehouse, Warehouse, Eventhouse) that stores property graphs in an open format (e.g., extending Delta with graph metadata, or adopting an open standard like Apache AGE or GraphAr), with full Purview lineage and OneLake shortcut support. Automatic graph construction pipelines — Data Factory / Fabric Notebook templates that extract entities and relationships from Silver-layer tables and unstructured documents using Azure OpenAI / Phi models, with human-in-the-loop validation.   Why It Matters Accuracy where it counts: Enterprise and regulated customers Unlocks Fabric's strategic differentiator: Fabric already unifies data estates in OneLake. Graph-RAG is the reasoning layer that turns that unified estate into a reasoning estate Reduces architectural sprawl: Customers currently stitch together Azure AI Search + Cosmos DB Gremlin + custom orchestration ... View more

Hi Power BI Team, I am writing this not just as a Data Engineer, but as someone who genuinely loves building stories with data. In my professional life, I work with complex APIs, write Python codes in PyCharm, and manage data structures in VS Code. My MacBook Pro is the heart of my creative and technical world. However, there is one piece missing in this puzzle: Power BI Desktop for Mac. Every time I need to make a quick fix or respond to an urgent data request on a weekend, I find myself struggling with virtual machines or switching back to my corporate Windows laptop just to open a .pbix file. My MacBook M3 has incredible power, and it feels like a waste not to be able to use the world's best BI tool natively on the world's most efficient hardware. I know the future is web-based and Fabric is amazing. But as engineers, we sometimes need that desktop experience: the offline capability, the advanced modeling, and the seamless integration. Please do not make us choose between the hardware we love and the software we rely on. Bringing a native Power BI experience to macOS would not just be a technical update; it would be a huge embrace for the growing community of data professionals who use Mac. With respect and hope, Fatma Nur Kurbanoglu Data Engineer ... View more

Customer wanted to know how they can get a notifications for upcoming Power BI preview features before they are pushed and enabled in the tenant settings. Not using the below options: Preview features are announced externally first (blogs, newsletters, events) Check Power BI updates blog (monthly drop) The most reliable proactive model is: Blog + Newsletter + Enablement sessions + RSS tracking     ... View more

Gen2 V2 CI/CD Dataflows currnelty require Contributor or higher access to the the workspace to consume tables. This will be a violation of permission restrictions to be compliant with CMMC, ITAR, Finance and other governmental infrastructure/data requirements. We need to limit user access to a workspace to Viewer access, but still be able to access Gen2 V2 CI/CD dataflow tables. Gen1 Dataflows allow consumption via Viewer access (which is desired), but they do not support Fast Load. With Fast Load from Gen2 Dataflows, some queries are running over 90% faster with lower overhead on our upstream cloud services/platforms. The alternative solution to share Gen2 V2 CI/CD dataflow tables by extending access via Semantic model is not sufficient as users are unable to use the source in Transforms as they can with Dataflow tables. ... View more

I have the following workspace setup in Microsoft Fabric: Production workspace containing: Lakehouses Semantic models Notebooks Copy Jobs These are used for production workloads. Additional workspaces: Test Development To support environment separation, I created corresponding lakehouses in the Development workspace and configured a Copy Job to copy data from the Production lakehouse to the Development lakehouse. This setup works correctly. Scenario To avoid manually recreating Copy Jobs, I attempted to duplicate an existing Copy Job by: Opening the original Copy Job JSON definition Copying the JSON Updating: The Copy Job name The Copy Job ID The target lakehouse ID Saving/closing the JSON editor After closing the editor, the Copy Job appears correctly configured and displays the expected settings. Problem However, after closing the Copy Job and reopening it, the Copy Job is completely empty — as if it were newly created. This suggests that: The edited JSON definition is not actually being persisted/saved Or the manually modified metadata/IDs invalidate the Copy Job without showing an error Expected Behavior After editing and saving the Copy Job JSON: The updated Copy Job definition should persist Reopening the Copy Job should display the saved configuration If certain properties (such as IDs) are not allowed to be modified, the UI/editor should provide validation errors or warnings Actual Behavior JSON edits appear accepted initially No validation or save errors are shown Reopening the Copy Job results in an empty/default Copy Job configuration Additional Notes This capability would be very useful for promoting or replicating Copy Jobs across Development, Test, and Production workspaces without needing to recreate them manually. ... View more

We would like to request support for a direct private connectivity path between Power BI Service and Azure Databricks, such as Azure Private Link (or an equivalent service-managed private endpoint), that bypasses the need for a VNet Data Gateway seralize/deserialize stage. Currently, when connecting Power BI Service to a Databricks workspace configured with private networking (no public endpoint), the only supported approach is through a VNet Data Gateway or On-premises Data Gateway. While this enables secure connectivity, it introduces additional overhead due to the serialization and deserialization of data through the gateway layer. ... View more

It would be nice if there is a way to send subscriptions directly in Fabric without workarounds based on a condition in the dataset . Example: Only send the email subscription when: MAX(Calendar[Date]) = TODAY() - 1 This would help ensure subscriptions are only sent when the required data is available or conditions are met. Thanks. ... View more

Problem Fabric ontology can be generated from a semantic model with one click — but customers still have to build the semantic model manually. For a lakehouse with 100–200 Gold tables, that's days of click-by-click work to select tables, define relationships, mark date tables, and create measures. Customers with 5,000+ tables and 300–800 ontology candidates often estimate 3–4 weeks of manual effort before they can even start with ontology. This is a major adoption blocker for Fabric IQ. What would help Any form of automation or AI-assisted bulk creation of semantic models directly from lakehouse metadata — using schema, profiling, lineage, or Copilot — would dramatically shorten the path to ontology. Even partial automation (e.g., relationship detection on user-selected tables) would be a meaningful first step. Anyway to automate the creation ? not fully but a base atleast ? Why it matters Snowflake (Semantic Views Autopilot), Databricks (Genie), dbt, and AtScale already automate this. Fabric is behind on this specific capability, despite having stronger downstream features (ontology graph, Activator, agents). Closing this gap would accelerate Fabric IQ adoption for the enterprise segment with the largest data estates. ... View more

While documentation for Dataflows Gen1 exists, clearer guidance on roadmap, support lifecycle, and migration expectations is needed, as Gen1 is now a legacy artifact. Although migration guidance to Gen2 is available, more detailed scenarios—such as migrating Dataflows Gen1 with linked entities (Linked dataflow gen1's) to Gen2—along with improved clarity, additional context and consolidation across documentation, would help plan and transition more effectively. ... View more

Introduce shared / reusable schedule objects that can be referenced by multiple Fabric artifacts (multiple pipelines, notebooks, and other artifacts), similar to SQL Server Agent job schedules. This would allow teams to define execution timing once (for example, a nightly or hourly schedule) and apply it consistently across many workloads, rather than duplicating identical schedules per artifact. Shared schedules would reduce configuration drift, simplify maintenance, and make schedule changes safer and faster by allowing updates in one place. This capability would significantly improve enterprise operations, governance, and migration scenarios for organizations moving from SQL Server Agent–based architectures to Fabric, where coordinated execution timing across many processes is a common requirement. ... View more