Power BI Embedded: Multiple Users Seeing the Last Logged-In District's Data
Hi, We have a Power BI report embedded in our .NET application. We use DirectQuery, and our SQL Server production environment contains around 1,800 databases. In Power BI, we pass a database parameter to connect the report to the appropriate district's database. Each user belongs to a specific district, and the report should display data only from their respective district/database. We are facing an issue when multiple users log in at the same time. For example, User A logs in from District A, and the report connects to the District A database and displays the correct data. Then User B logs in from District B, and the report connects to the District B database. After User B logs in, User A also starts seeing District B data. It appears that the last-loaded database parameter value is being used by all users instead of maintaining a separate database parameter value for each user's embedded report session. We are using Power BI Embedded with a .NET application and DirectQuery, with approximately 1,800 SQL databases in production. What is the recommended approach for this scenario? How can we ensure that each user's embedded Power BI report independently uses the correct database parameter without affecting other users? Would Dynamic RLS, effective identity, separate embed tokens, or another Power BI Embedded approach be appropriate for this architecture? Any guidance on how Power BI handles parameters and DirectQuery connections for multiple concurrent embedded users would be greatly appreciated. Thanks!
Direct Lake on OneLake + Deployment Pipelines + Git: parameter rules deploy but semantic model refre
Hi all, I’m trying to make a Fabric / Power BI semantic model deployment flow work for Dev -> Prod with Git and Deployment Pipelines, but I’m stuck on Direct Lake on OneLake source rebinding. Setup: I have two Fabric workspaces: DEV workspace: TPA-BI-Hub-Dev DEV lakehouse: lh_tpa_bi_hub PROD workspace: TPA-BI-Hub PROD lakehouse: lh_tpa_bi_hub The semantic model is a Direct Lake on OneLake model. In TMDL, the source expression uses: AzureStorage.DataLake( "https://onelake.dfs.fabric.microsoft.com/<workspace-id>/<lakehouse-id>", [HierarchicalNavigation=true] ) Tables have Direct Lake partitions, for example: partition dim_kontakt = entity mode: directLake source entityName: dim_kontakt schemaName: gold expressionSource: 'DirectLake - lh_tpa_bi_hub' Microsoft docs say Direct Lake on OneLake does not directly support deployment pipeline data source rebinding rules, but a parameter expression can be used in the connection string. Reference: https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-overview Attempt 1: split parameters We changed the expression to: AzureStorage.DataLake( "https://onelake.dfs.fabric.microsoft.com/" & WorkspaceId & "/" & LakehouseId, [HierarchicalNavigation=true] ) with parameter rules in the Production stage: WorkspaceId = <PROD workspace id> LakehouseId = <PROD lakehouse id> The model deployed and showed data, but refresh/reframe failed with: DirectQuery schema evaluation is not supported by the service-side ServiceLegacySchemaEvaluations. DirectQuery schema evaluation is a desktop-only path. Attempt 2: single full URL parameter Based on community examples, we changed to: expression 'DirectLake - lh_tpa_bi_hub' = let Source = AzureStorage.DataLake(#"OneLakeSource", [HierarchicalNavigation=true]) in Source expression OneLakeSource = "https://onelake.dfs.fabric.microsoft.com/<DEV workspace id>/<DEV lakehouse id>" meta [IsParameterQuery = true, Type = "Text", IsParameterQueryRequired = true] annotation PBI_ResultType = Text Then Production parameter rule: OneLakeSource = https://onelake.dfs.fabric.microsoft.com/<PROD workspace id>/<PROD lakehouse id> But Fabric validation failed with: ConceptualSchemaSettings should not be provided for non-Direct Lake queries That sounds like the service no longer classifies the parameterized expression as Direct Lake, even though the table partitions are still mode: directLake. Questions: 1. What is the recommended Git-compatible Dev -> Prod deployment pattern for Direct Lake on OneLake semantic models? Specifically: 2. Is parameterizing AzureStorage.DataLake(...) officially supported for semantic model refresh/reframe in the Power BI service, or only for deployment-time rule substitution? 3. Is the single URL parameter pattern expected to work with Git-integrated semantic models and Deployment Pipelines? 4. If parameter rules are unreliable here, is the recommended approach to maintain environment-specific branches, e.g.:develop has static DEV OneLake URL main / release-prod has static PROD OneLake URL 5. Or should we migrate to Direct Lake on SQL endpoint to use deployment pipeline data source rules properly? Related sources I found: Microsoft Direct Lake overview: https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-overview Microsoft Direct Lake development/TMDL source types: https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-develop Community example using OneLakeURI parameter: https://www.datazoe.blog/post/deployment-pipelines-and-power-bi-semantic-models-with-direct-lake-on-onelake-tables Direct Lake on OneLake vs SQL identification: https://data-mozart.com/direct-lake-models-are-they-onelake-or-sql-and-how-to-check/ Any confirmation of a working production-grade pattern would be very helpful.
Couldnt authenticate web content(REST API)
Hi, We have created salesforce REST API that is querying the objects using SOQL and then passes it in JSON format. I am now trying to use the URL in power bi desktop and its giving me an error : "we couldnt authenticate with the credential provided". I have already tried Basic login. How we can create the authentication to use this REST API in power bi?
