Explore and analyze data
Query, visualize, and diagnose
Exploring and analyzing data in Fabric spans several surfaces. Use the SQL analytics endpoint for T-SQL over lakehouse tables, KQL querysets for real-time data, notebooks for PySpark profiling, and Data Wrangler for quick visual transforms. In Power BI, DAX query view runs EVALUATE queries against a semantic model and is ideal for validating measures; Performance Analyzer and query diagnostics show where time is spent. Visual exploration should start with profiling: row counts, distinct values, nulls, and distributions before building visuals.
DAX analysis depends on filter context. A measure like Total Sales = SUM(Sales[Amount]) returns different values by slicer, visual axis, or CALCULATE modifier. Use SUMMARIZECOLUMNS or a matrix visual to group; use DIVIDE for safe ratios. For Direct Lake models, check whether a visual triggered fallback to DirectQuery before blaming the visual.
Traps: putting a measure in a calculated column; expecting a measure to filter rows without CALCULATE; confusing EVALUATE query results with report visuals; and assuming a slow visual is a DAX problem when the bottleneck is the source query or model relationships.
Worked example: to find the top five products by sales, create a measure Total Sales, then EVALUATE TOPN(5, SUMMARIZECOLUMNS(Product[Name], "Sales", [Total Sales]), [Sales], DESC). Verify the measure in DAX query view, then build the visual.