Solutions for Data Science

AI-driven interaction with semantic models is starting to reshape the Power BI conversation. With Microsoft introducing MCP server capabilities (Preview), AI agents can interact directly with models — reducing the gap between a business question and a data response. From the perspective of delivering Power BI solutions across multiple enterprise clients, the potential is clear — but so are the responsibilities. Where this can help • Lower barrier for business users to explore data • Faster insight cycles through conversational access • Streamlined development and analytical workflows Where caution is required 🔐 Security, governance, and guardrails When AI agents interact with live semantic models, the exposure surface expands. This isn’t just about authentication — it’s about: • Enforcing robust role-based access and data segmentation • Monitoring query behavior and usage patterns • Preventing unintended access paths to sensitive datasets • Establishing guardrails around agent c...

Power BI Version Control — What Microsoft Recommends (and Why It Matters) If you're developing Power BI content in a team, stop treating .pbix files like final artifacts. Start treating Power BI like a software product. ✅ Use .pbip (Power BI Project) instead of .pbix ✅ Integrate with Git for proper source control ✅ Work in feature branches ✅ Merge via Pull Requests ✅ Deploy across Dev → Test → Prod workspaces Why this shift? .pbix is a binary file. You can't diff it. You can't properly review changes. You can't merge it cleanly. .pbip is text-based and folder-structured, separating: Report metadata Semantic model definition DAX queries Settings and resources This enables: Version history tracking Code reviews CI/CD pipelines Automated validation Scalable team collaboration Modern BI teams should operate like engineering teams. Governance, branching strategy, environment promotion, and source control are no longer optional — especially in Microsoft...

40 Power Query Editor features in Power BI along with examples: 1. Filter Rows: Remove rows based on conditions. Example: Remove rows with a null value in the "CustomerName" column. 2. Remove Duplicates: Eliminate duplicate rows. Example: Remove duplicate entries based on the "OrderID" column. 3. Sort Rows: Arrange rows in ascending or descending order. Example: Sort data by "Date" column in descending order. 4. Replace Values: Substitute one value with another. Example: Replace "N/A" with "Unknown" in the "Status" column. 5. Split Columns: Divide a column into multiple columns. Example: Split "FullName" into "FirstName" and "LastName." 6. Merge Queries: Combine data from multiple sources. Example: Merge customer and order data based on the "CustomerID." 7. Group By: Aggregate data based on a specific column. Example: Group sales data by "ProductCategory" and calculate the sum ...

20 Time Intelligence DAX measures in Power BI with examples: Year-to-Date Sales: css Copy code YTD Sales = TOTALYTD( [Total Sales] , Calendar [Date] ) Month-to-Date Sales: css Copy code MTD Sales = TOTALMTD( [Total Sales] , Calendar [Date] ) Quarter-to-Date Sales: css Copy code QTD Sales = TOTALQTD( [Total Sales] , Calendar [Date] ) Previous Year Sales: mathematica Copy code Previous Year Sales = CALCULATE ( [ Total Sales ] , SAMEPERIODLASTYEAR ( Calendar [ Date ] ) ) Year-over-Year Growth: css Copy code YoY Growth = DIVIDE( [Total Sales] - [Previous Year Sales] , [Previous Year Sales] ) Rolling 3-Month Average Sales: sql Copy code 3 M Rolling Avg Sales = AVERAGEX(DATESINPERIOD(Calendar[ Date ], MAX (Calendar[ Date ]), -3 , MONTH ), [Total Sales]) Cumulative Sales: scss Copy code Cumulative Sales = SUMX (FILTER(ALL(Calendar), Calendar [Date] <= MAX (Calendar[Date])), [Total Sales] ) Running Total Sales: scss Copy code Running Total Sales = SUMX (FILTER(ALL(Calendar), Calen...