How do you usually troubleshoot slow Power BI reports?
I work as a Power BI Analyst at Edoxi, and through my work with Power BI reports and data visualisation, I’ve found that report performance is not always related to dataset size. Even a relatively small model can become slow because of complex DAX measures, inefficient relationships, too many visuals, or expensive Power Query transformations. When troubleshooting performance, I usually start by checking these areas: Data model — Check whether the model is structured appropriately, such as using a star schema where suitable. DAX measures — Review calculations and iterators that may be unnecessarily complex. Relationships — Check cardinality, filter direction, and any ambiguous relationships. Visuals — Look at the number of visuals and interactions on the report page. Performance Analyzer — Identify which visual or query is taking the most time. Power Query — Review expensive transformations and check whether query folding is maintained where applicable. I’ve found that identifying the actual bottleneck first is more effective than changing multiple parts of a report at the same time. For other Power BI professionals and analysts, what performance issue do you encounter most often: DAX, data modelling, Power Query, or report design?
Web.Contents failed to get contents from 'https://ausreman.tidywork.com/api/materials' (400)
I have tried a number of scenarios for posting data, but I have come up with nothing. I am trying to write a power query that posts data to a REST API (https://ausreman.tidywork.com/api/materials), it posts data for materials. The current query I have is: = Json.Document(Web.Contents("https://ausreman.tidywork.com/api/materials",[Headers= [#"Authorization"="Basic ZZZZZZZZZZZZZZZZZ",#"Content-Type"="application/json"], Content=Json.FromValue({[projectId="65b6d1fe-ec2e-47c3-9c92-2aad8fdd1d26",userId="67220adb-d5cb-4667-a3ce-ebe3d6248334",materialDateUtc="2015-09-14T07:11:00",materialDate="2015-09-14T17:11:00",materialId="00000000-0000-0000-0000-000000000000",materialLocationId="1aeb9552-a3e9-4804-a7b2-641b290dac8d",materialItemCode="1H3338",materialItemId="f9be8264-81f0-4ae2-a12b-569b3e05d034",costCurrencyId="17709522-6b2e-4b01-9cdf-bb7eb0a43c52",chargeCurrencyId="17709522-6b2e-4b01-9cdf-bb7eb0a43c52",taskId="b2ffc2cf-326a-fc91-eb40-c4ddb4147a35",state="Approved",usageQuantity=1,quantity=1,cost=0.21,charge=0.25]})]), 1252) The only thoughts I had was that it had something along the lines of the size of the data being post. Please help me remove this frustration.
Power BI model suddenly failing due to circular dependency issue
Hi team , My Power BI model was working fine until recently, but it has now started failing with a circular dependency error. I tried modifying the formula it was pointing to, but this triggered issues across many other calculated fields that depend on it, making it very difficult to update all of them without causing data discrepancies. Is there a way to resolve this circular dependency issue without modifying the existing DAX logic? Any guidance would be appreciated.
Workaround for OLS related Limitations
Hello Everyone, When we are dealing with OLS in our semantic model. We came up limitations, i would like to know if any workarounds. Before OLS implementation our Model Structure: Role A can see Column A. Role B can see both Column A and Column B. We use Column A and Column B in a same metrics & toggle between both based on the Logged in Roles. We used a Metrics to toggle between Column A and Column B. (Reason is both column meant for alike data display, eg, Profit and Profit_withMargin) We have 700 metrics. Now Challenge during OLS implementation: If we apply OLS in our model, When Role A logsin our metrics break because we Column B in the metric is been filtered by OLS feature. To overcome this we have two solution. One to create metrics seperately for each role. This will be heavy maintanence activity if any new role got added in future. Second to depivot the table in ETL. (Ex: Keep both Profit and Profit_withMargin in same column) and apply RLS. This will increase datasize alot. Chances of getting performance and load issue are high) Any one face similar issue and have anothe quick fix workaround? Please let me know. Thanks in Advance.
