Follow the complete workload

A user interaction can touch report design, DAX, semantic relationships, storage mode, generated SQL, warehouse sizing, clustering, and the shape of the data being queried. Each layer can amplify or hide a weakness in another.

Useful diagnosis begins with representative user actions and refreshes, not isolated platform benchmarks.

  • Report interaction and visual query behavior
  • DAX and semantic-model structure
  • Generated SQL and source-model grain
  • Warehouse workload, sizing, and concurrency
  • Refresh, caching, and deployment patterns

Modeling is often the shared boundary

Power BI performs best when the source and semantic model agree about grain, reuse, and where calculations belong. Snowflake can answer expensive queries quickly, but repeatedly asking it to reconstruct analytical structure at interaction time is still costly.

The goal is not to push everything into one platform. It is to place transformations, aggregations, measures, and security where they remain understandable and efficient.

Optimize against a baseline

Measure the same report interactions and refreshes before and after a change. Keep the data, filters, access role, cache conditions, and concurrency comparable. Reconcile the figures as well as the timings.

Use the baseline to detect regressions after release. A faster warehouse query does not, by itself, establish a faster reporting experience.

Primary sources

Related services and experience

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