Disappearing data on combo chart (line + bar chart)
Hi all, I'm facing an issue, I'm using a Line and clustered column chart, but what's happening is, when I add an average line which shows correctly calculated, my december month simply disappears. You can find the measures I'm using below. Have anyone faced an issue like this? With average line: Without average line: I couldn't figure out why. Measures: _Sales Trend Delta % Avg (Year) = VAR Yr = INT ( MAX ( dCalendar[YearMonthKey] ) / 100 ) RETURN CALCULATE ( AVERAGEX ( VALUES ( dCalendar[YearMonthKey] ), [_Sales Trend Delta %] ), REMOVEFILTERS ( dCalendar ), FILTER ( ALL ( dCalendar[YearMonthKey] ), INT ( dCalendar[YearMonthKey] / 100 ) = Yr ) ) _Sales Trend Delta % = DIVIDE ( [_Sales Trend CY] - [_Sales Trend PY], [_Sales Trend PY] ) _Sales Monthly CY = VAR StartD = [_Sales Start Value] VAR EndD = [_Last Full Month End] VAR StartKey = YEAR ( StartD ) * 100 + MONTH ( StartD ) VAR EndKey = YEAR ( EndD ) * 100 + MONTH ( EndD ) VAR CurrType = SELECTEDVALUE ( Sales[Currency Type], 30 ) RETURN CALCULATE ( AVERAGE ( Sales[Sales] ), KEEPFILTERS ( dCalendar[YearMonthKey] >= StartKey && dCalendar[YearMonthKey] <= EndKey ), Sales[Currency Type] = CurrType ) _Sales Monthly PY = VAR StartD = [_Sales Start Value] VAR EndD = [_Last Full Month End] VAR StartKey = YEAR ( StartD ) * 100 + MONTH ( StartD ) VAR EndKey = YEAR ( EndD ) * 100 + MONTH ( EndD ) VAR CurrType = SELECTEDVALUE ( Sales[Currency Type], 30 ) VAR PYKeys = SELECTCOLUMNS ( FILTER ( VALUES ( dCalendar[YearMonthKey] ), dCalendar[YearMonthKey] >= StartKey && dCalendar[YearMonthKey] <= EndKey ), "@Key", dCalendar[YearMonthKey] - 100 ) RETURN CALCULATE ( AVERAGE ( Sales[Sales] ), REMOVEFILTERS ( dCalendar ), TREATAS ( PYKeys, dCalendar[YearMonthKey] ), Sales[Currency Type] = CurrType ) _Sales Start Value = IF ( ISFILTERED ( 'Sales Start Date'[Date] ), MIN ( 'Sales Start Date'[Date] ) -1, DATE ( 2022, 1, 1 ) ) _Last Full Month End = EOMONTH ( [_As Of Date Value], -1 ) _As Of Date Value = IF ( ISFILTERED ( 'As Of Date'[Date] ), MIN ( 'As Of Date'[Date] ), CALCULATE ( MAX ( 'As Of Date'[Date] ), REMOVEFILTERS ( 'As Of Date' ) ) ) TABLE: As Of Date = CALENDAR ( DATE ( 2020, 1, 1 ), TODAY () )
Gantt 3.0.12.0
Hello - I am trying to create a simple gantt chart with worksteam > Product> Epics with duration and the group tasks layer them all ontop one another. Can someone help me with layering the epics in the right Product workstream to show the gantt landscape like picture #2? This is with the Promo gantt which is paid for :https://www.vibecode-studio.nl/powerbivisuals/promo-gantt but it layers them correctly as the output which is what I am intending for.
Dynamic Refresh Period Per Firm - Date Slicer Shows Global Range Instead of Firm-Specific Range
Hey all, I have a transactions dataset with multiple firms where each firm needs a different data retention period. Firm A needs 12 months of history, while Firm B needs 15 months. Users are scoped to one firm at a time via RLS. The issue: Even though the fact table correctly loads firm-specific months from Snowflake, the date slicer shows the same global date range (15 months) for both firms. Current Setup: Parameters: RefreshPeriod = "12" (default months) RefreshPeriodOverrides = "FirmB:15" (firm-specific overrides) FirmRefreshCaseSQL = generates CASE WHEN FIRM_KEY = [FirmB] THEN -15 ELSE -12 END MaxRefreshPeriod = "15" (max across all firms) Data Loading: Transaction fact table: Uses FirmRefreshCaseSQL in WHERE clause, correctly loads 12 months for Firm A and 15 months for Firm B Calendar dimension: Uses MaxRefreshPeriod, loads 15 months globally Relationships: Transaction[Transaction Date] → Calendar[Transaction Date] (many-to-one, single direction) Transaction[FIRM_KEY] → Firm[Firm ID] (many-to-one) Date Slicer: Bound to Calendar[Transaction Date] Expected Behavior: When Firm A is selected: slicer shows last 12 months When Firm B is selected: slicer shows last 15 months Actual Behavior: Both firms show last 15 months because the calendar dimension is global and doesn't know which dates belong to which firm What I've Tried: ✗ Setting parameter overrides at the report level - didn't propagate to semantic model ✗ Testing with measures - confirmed fact data is correct per firm, but slicer uses calendar dimension 🔄 Current approach: Adding a "Firm-Date Availability Bridge" table with distinct (FIRM_KEY, Transaction Date) combinations to enable filter propagation: Firm → Bridge → Calendar Questions: Is a bridge table the right approach for this scenario, or is there a simpler pattern? Should I consider making the Date↔Transaction relationship bidirectional instead? (Concerned about RLS implications) Are there any DAX-only solutions that don't require model changes? Has anyone solved firm-specific date filtering without loading separate datasets per firm? Any guidance would be appreciated! Thank you!
Connecting to a semantic Model issue
Hello, We created a Snowflake mirrored database in Microsoft Fabric. User A then created a semantic model connected to this mirrored database (OneLake). We have an older semantic model, and I migrated all the measures from it to the new semantic model using TMDL Apply. However, when I tried to connect to User A's semantic model from Power BI Desktop to create reports and visuals, I received the following error: Both User A and I are Workspace Administrators. To troubleshoot, I created my own semantic model on the same mirrored database, migrated the same measures using TMDL Apply, and then connected to it from Power BI Desktop. In this case, the connection worked successfully without any issues. My question is: Why am I unable to connect to the semantic model created by User A, even though we have the same workspace permissions? What could explain the difference in behavior between User A's semantic model and the one I created, given that both are in the same workspace, use the same mirrored database, and have the same measures migrated via TMDL? Any insights or recommendations would be greatly appreciated.Validation of my understanding of Fabric Semantic Models, Power BI Desktop, and Workspace Scenarios.
Validation of Microsoft Fabric Semantic Model Architecture, Connection Modes, and Power BI Desktop Behavior Hi everyone, I've been exploring different Microsoft Fabric and Power BI integration scenarios and would like to validate my understanding. I'd also appreciate clarification on a few concepts related to Semantic Models, connection modes, and Power BI Desktop. 1. End-to-End Fabric Architecture Data Sources ↓ Fabric Lakehouse / Warehouse ↓ Centralized Semantic Model ↓ Power BI Reports / Paginated Reports My understanding is: Data is ingested into Fabric Lakehouse or Warehouse. A centralized Semantic Model is created on top of the data. Multiple reports can consume the same Semantic Model. This is the recommended enterprise architecture for governance, scalability, and reusable business logic. Is this correct? 2. Fabric Semantic Model → Power BI Desktop Fabric Semantic Model ↓ Power BI Desktop ↓ Report My understanding is: Power BI Desktop connects to the published Semantic Model using a Live Connection. Reports can be developed in Power BI Desktop. The Semantic Model itself cannot be modified because it is centrally managed in Fabric. However, whenever I connect to a Fabric Semantic Model from Power BI Desktop, Desktop opens in Semantic Model editing mode, and I don't see the Report View. My questions are: Is this expected behavior? Why is Report View not available? Is there a way to create reports directly from the same Power BI Desktop session after connecting to a Fabric Semantic Model, or is this by design? What is the recommended workflow for report developers in this scenario? 3. Power BI Pro Workspace and Fabric My understanding is: A standard Power BI Pro workspace cannot host Fabric artifacts such as Lakehouses, Warehouses, Notebooks, or Pipelines because they require Fabric capacity. However, a Pro workspace can consume an existing Semantic Model published from a Fabric workspace (subject to permissions and licensing). Reports can then be created using that Semantic Model. Is this correct? 4. Connecting Power BI Desktop to a Fabric Warehouse I believe there are two approaches. Approach 1 – Semantic Model Development Fabric Warehouse Tables ↓ Semantic Model ↓ Power BI Desktop Used for Semantic Model development. Opens in Semantic Model editing mode. Report View is not available. Approach 2 – Direct Connection Fabric Warehouse Tables / SQL Objects (Tables, Views, etc.) ↓ Power BI Desktop ↓ Semantic Model ↓ Report Connect directly to the Fabric Warehouse. Use Warehouse tables or SQL objects such as views. Power BI Desktop provides the normal report authoring experience. Semantic Model and reports are developed together. Is this understanding correct? 5. Connection Modes within a Fabric Semantic Model I'm also trying to understand how the following storage/connection modes work in Fabric Semantic Models. Import DirectQuery Direct Lake My questions are: Can all three connection modes be used when creating a Semantic Model in Fabric? What are the recommended scenarios for each mode? Are there any limitations depending on whether the Semantic Model is built from a Lakehouse or a Warehouse? Can these modes be mixed within a single Semantic Model? 6. Refresh Behavior and Data Pipeline I'd also like to understand how Semantic Model refresh works in a complete Microsoft Fabric solution. Assuming data is loaded into a Lakehouse or Warehouse through any Fabric ingestion mechanism, such as: Dataflow Gen2 Notebooks Pipelines Mirroring Shortcuts Other supported Fabric ingestion methods My understanding is that all of these eventually populate the Lakehouse or Warehouse, which is then consumed by the Semantic Model. My questions are: Is this understanding correct? Does the Semantic Model refresh behavior remain the same regardless of whether the data was loaded using Dataflow Gen2, Notebooks, Pipelines, or any other Fabric ingestion process? How does Semantic Model refresh work for each storage mode: Import DirectQuery Direct Lake Is a scheduled Semantic Model refresh always required, or does it depend on the storage mode? For Direct Lake, when new data is written into the Lakehouse or Warehouse, does the Semantic Model automatically reflect those changes, or is some form of model refresh still required? Are there any best practices for orchestrating data refresh and Semantic Model refresh within Microsoft Fabric? 7. Cross-Workspace Scenarios Is my understanding correct? Fabric Workspace Semantic Model → Fabric Report → Supported Fabric Workspace Semantic Model → Power BI Desktop Report (Live Connection) → Supported Power BI Workspace Semantic Model → Fabric Report → Supported Power BI Workspace Semantic Model → Power BI Report → Supported 8. Semantic Model Terminology One area I'm still trying to clarify. Is there technically any difference between a Fabric Semantic Model and a Power BI Semantic Model, or are they actually the same Semantic Model artifact, with the only difference being where they are created or hosted? I'd appreciate any corrections or clarifications. My goal is to build a solid conceptual understanding of Microsoft Fabric architecture, Semantic Models, and the recommended enterprise development workflow. Thank you!
Prod Workspace Usage Report Semantic Model Update Issue
Hi Team, I downloaded the new usage report from power bi production workspace, but after adding new kpis to it and publishing it. I can't see that is being updated. I can't even revert it. Please guide me if it is possible to delete that exisiting semantic model of power bi for usage report and whether it is possible to resolve conflict or restore default version of semantic model of usage report.
