Microsoft Fabric for GCC data operations: how would you structure the platform for scale?
A GCC implementation we examined involved more than just implementing a data platform; it required the realization of a scalable data engineering function to adapt to growing data volumes, expansion of pipelines, reporting, and business teams. The ecosystem consisted of many data sources and data engineering tasks, with teams focusing on aspects such as data ingestion, transformation, quality control, pipeline supervision, analytics, and platform support. With the GCC progressing, a key question arose: how to harmonize the workloads without an excessive increase in operational costs? If I were asked to implement this today using Microsoft Fabric, I would like to see how many processes of the stack could be combined into one solution instead of having separate services at each stage. An available solution might be: OneLake and Lakehouse for the central data hub with a reliable data source Data Factory/Dataflows Gen2 used to ingest and transform data Fabric Notebooks for complex data engineering, validations, and transformations Pipelines for managing, scheduling, connecting, and tracking the process Power BI and Direct Lake for creating reports based on selected data Microsoft Purview for data management and governance processes Access control with RBAC to work with development, testing, and production processes Git integration and pipelines to have more organized engineering and release management However, based on my experience with data solutions, I believe there is no question whether Fabric will be able to create and support the above-mentioned environment, but about engineering consistency. It is essential to establish definite standards regarding: Naming conventions and the architecture of the workspace Medallion and layered designs Patterns for ingestion Validation and quality checks Balancing failure and retry events Monitoring alerts and behvaviors Modern practices in CI/CD and versioning Who owns datasets, models and governance policies One of the most important things here is how one can staff his/her engineering team. More engineers do not mean that you are creating a scalable data platform. If you fail to put standard patterns in place, you may end up getting more than one pipeline, duplicated transformations, no workspace management and various datasets. One thing I would be especially interested in finding out from the Fabric community is the way non-centralized governance is effectively combined with the level of autonomy allowing GCC dev teams to work without inconveniences. For those running Microsoft Fabric at scale, how are you structuring your workspaces, CI/CD, and engineering standards when multiple data engineering teams are contributing to the same Fabric environment?
Table level or Columns level lineage
Hallo Community, is it possible to trace the source tables and columns used in a report? I understand that intermediate transformations can change the tables and columns during the process. However, I would like to know if there is any workaround or best practice to trace the fields in a report back to their original source tables and columns. We are having Medallion architecture and using Noteboos (pyspark) to transform the data. Any tips or suggestions would be greatly appreciated.Salesforce data in Fabric with bronze/silver/gold: what would you change?
Hi community, I've recently built a Salesforce analytics setup on Microsoft Fabric with my team at datatobiz and I'd like to hear how you would do differently. Here is the setup: Ingestion: Dataflow Gen2 connects Salesforce to Fabric, with scheduled, incremental loads for objects such as Accounts, Opportunities, Orders, Products, Campaigns and Cases Bronze: raw Salesforce tables land in OneLake with the schema preserved, for auditability and lineage Silver: Fabric and Databricks notebooks handle duplicates, data types, business rules and object relationships Gold: subject-specific marts for Sales Performance, Customer 360 and Operations Pipelines: scheduled refreshes with monitoring and alerts Reporting: Power BI semantic models on star schemas, with DAX measures and role-based views Governance: Azure AD role-based access, Microsoft Purview lineage and Azure Key Vault I'd like your views on: Is Dataflow Gen2 a good fit for incremental Salesforce loads, or would you use Copy activity or notebooks instead? Has anyone used Fabric and Databricks notebooks together in a silver layer? How did it work for lineage and monitoring? What data quality checks do you automate between bronze, silver and gold? Would you change anything in this layering for Salesforce data? Thanks in advance!
Capacity Metrics schema validation failed for all supported versions in FUAM
Hello, I am facing an issue while collecting data from the Microsoft Fabric Capacity Metrics App using the FUAM notebook code. The schema compatibility validation fails for every version checked by the notebook: INFO: Test for v53 failed INFO: Test for v47 failed INFO: Test for v40 failed INFO: Test for v37 failed The notebook then returns the following exception: ERROR: Capacity Metrics data structure is not compatible or connection to capacity metrics is not possible. Could someone please help clarify: Whether the Capacity Metrics App schema has recently changed. Whether the current FUAM release supports the latest Capacity Metrics App. Whether a newer validation query or updated FUAM notebook is available. Thank you.
Power Automate Export to PDF Returns Blank Report for Report Connected to Shared Semantic Model
Hi Team, I am facing an issue with Power Automate's "Export To File for Power BI Reports" action. Scenario I have a Power BI Semantic Model (Dataset A). Report A is built directly on Dataset A. Report B is another report built using the same semantic model(shared dataset/thin report approach). Expected Behavior When Power Automate exports Report B to PDF, the report should contain the same data that is visible in Power BI Service. Actual Behavior Exporting Report A through Power Automate generates a PDF with data correctly displayed. Exporting Report B through Power Automate generates a PDF, but the visuals are blank and no data is shown. There are no export errors. Additional Findings Manual export from Power BI Service (File > Export > PDF) works correctly for both reports and the generated PDF contains data. The issue only occurs when exporting through Power Automate. Both reports use the same semantic model. The semantic model is accessible and contains data. If I export pages from Report A, data is visible in the PDF. If I export pages from Report B (connected report/thin report), the PDF is blank. Questions Does the Export To File for Power BI Reports action have any limitations with thin reports or reports connected to a shared semantic model? Are there any permission requirements (Build permission, RLS, semantic model access, etc.) that differ between manual export and Power Automate export? Has anyone experienced blank PDF exports when using a report connected to an existing semantic model while manual exports continue to work? Any guidance would be appreciated. Thank you.
Fabric Link Setup
Hello Community, I'm trying to Configure Fabric Link with FnO to sync My FnO data to Fabric. While creating Fabric Link for FnO I'm getting 2 Options as per below SS. One is F&O Entities (Caption 1) Second is F&O tables (Caption 2). I want to Understand How "FnO entities" and "FnO tables" different from each other, In which case I need to choose from second Option(F&O tables Caption 2) & in which case I need to choose from (F&O Entities Caption 1). Thank youFabric Copy Activity fails in Query mode but succeeds in Table mode through an on-premises gateway
Hi Fabric Community, I am using a Microsoft Fabric pipeline Copy Activity to copy data from a Fabric Lakehouse to an on-premises SQL Server database through a dedicated on-premises data gateway. I tested the Copy Activity using both of the following source connection types: Lakehouse connection Lakehouse SQL analytics endpoint connection With both connection types, the behavior is the same: Lookup Activity: Successful Script Activity: Successful Copy Activity using Table mode: Successful Copy Activity using Query mode: Fails When the Copy Activity source is configured with Use query = Query, it fails with the following error: ErrorCode=SqlFailedToConnect,'Type=Microsoft.DataTransfer.Common.Shared.HybridDeliveryException,Message=Cannot connect to SQL Database. Please contact SQL server team for further support. Server: 'xxxxxxxx.datawarehouse.fabric.microsoft.com', Database: 'LH_XYZ', User: ''. Check the connection configuration is correct, and make sure the SQL Database firewall allows the Data Factory runtime to access.,Source=Microsoft.DataTransfer.ClientLibrary,''Type=System.Data.SqlClient.SqlException,Message=A network-related or instance-specific error occurred while establishing a connection to SQL Server. The server was not found or was not accessible. Verify that the instance name is correct and that SQL Server is configured to allow remote connections. (provider: Named Pipes Provider, error: 40 - Could not open a connection to SQL Server),Source=.Net SqlClient Data Provider,SqlErrorNumber=53,Class=20,ErrorCode=-2146232060,State=0,Errors=[{Class=20,Number=53,State=0,Message=A network-related or instance-specific error occurred while establishing a connection to SQL Server. The server was not found or was not accessible. Verify that the instance name is correct and that SQL Server is configured to allow remote connections. (provider: Named Pipes Provider, error: 40 - Could not open a connection to SQL Server),},],''Type=System.ComponentModel.Win32Exception,Message=The network path was not found,Source=,' However, when I change the source configuration from Query to Table and select the source table directly, the Copy Activity completes successfully. Has anyone encountered this behavior? Does Query mode use a different runtime, connector path, or metadata-discovery process when the Copy Activity runs through an on-premises gateway? Are there any known limitations or additional gateway requirements for using a custom query as the source? Any guidance on how to diagnose or resolve this would be appreciated.Fabric Pipeline Error
Today, we noticed that our production pipeline started failing with the following error: Error: BadRequest Error fetching pipeline default identity userToken{ "code": "LSROBOTokenFailure","message": "AADSTS50173: The provided grant has expired due to it being revoked, a fresh auth token is needed. The user might have changed or reset their password. The grant was issued on '2026-08-24T05:50:05.7907970Z' and the TokensValidFrom date (before which tokens are not valid) for this user is '2026-09-18T16:05:07.0000000Z'. Trace ID: 250ed633-08f0-4dcb-bad0-11596983e901 Correlation ID: 430f0833-fbc4-44f8-b19a-883ada2cad74 Timestamp: 2026-09-18 18:29:30Z", "target": "PipelineDefaultIdentity-3e591cb6-2704-49f2-9efd-7196287c9feb","details": null,"error": null }FetchUserTokenForPipelineAsync Has anyone encountered this issue before or have any idea what could be causing it? Any suggestions on how to resolve this would be appreciated.
Claude Code Integrate with Power Bi
Hi everyone, I’m working on a POC for Power BI + Claude Integration, specifically the Report Authoring skill.We need to use Claude Code to create the required report pages and visuals. I’m currently trying to estimate the Claude Code credit/token consumption and approximate cost for completing this POC. Has anyone worked on a similar POC using Claude Code? If so, could you please share: Approximate credits/tokens consumed Estimated cost Any recommendations for estimating the usage before starting Any guidance or experience would be really helpful. Thanks!
