Power BI: Speed Up DAX Queries

Key insights

• YouTube video summary: This short summary covers a video explaining 2025 Power BI updates that speed up DAX calculations and make analysis faster.
  
• DAX: DAX is Power BI’s formula language for calculations like time intelligence and cumulative totals; faster execution delivers quicker insights.
  
• Power BI Copilot: Copilot uses AI to suggest and generate optimized DAX measures, explain functions, and cut manual coding time for users at all levels.
  
• Direct Lake: Direct Lake storage mode queries data directly in lakehouses, reducing data movement and improving query speed on large datasets.
  
• Performance updates: Recent desktop and service improvements lower latency and make complex DAX expressions run more smoothly and reliably.
  
• Optimization best practices: Simplify DAX where possible, tune your data model and source queries (especially in DirectQuery), and use Copilot suggestions to avoid bottlenecks.
  

Introduction

The YouTube video by How to Power BI summarizes key 2025 updates that aim to make DAX calculations faster and easier to author in Power BI. Reportedly, Microsoft combined AI assistance with storage and engine improvements to speed up common analytical workflows. As a result, everyday report authors and data teams may see shorter development cycles and quicker time to insight. This article objectively reviews those claims and explores the tradeoffs and challenges involved.

AI-assisted DAX authoring: Copilot steps in

First, the video highlights improved integration of Copilot to help write and explain DAX expressions, which can reduce manual coding time and lower the learning curve for new users. By suggesting optimized queries and generating business-focused measures, Copilot promises faster measure creation and clearer explanations for complex logic. However, relying on AI suggestions introduces tradeoffs: while beginners benefit from speed and guidance, teams must still validate performance and correctness to avoid hidden inefficiencies. Therefore, Copilot serves as an accelerator rather than a full replacement for careful model design and testing.

Storage and querying: Direct Lake and semantic models

The video also discusses support for semantic models in Direct Lake storage mode, which allows Power BI to query data near its source in lakehouse storage without costly imports. Consequently, this approach reduces data movement and can dramatically lower query latency on very large datasets when compared to import-only models. Nevertheless, Direct Lake shifts some responsibility to storage and query optimization, meaning that not all datasets will automatically perform better and some scenarios still favor imported, in-memory models. Thus, organizations must weigh storage costs, latency requirements, and data freshness needs when choosing a strategy.

Performance and developer experience improvements

Beyond AI and storage, the video notes general performance upgrades to the Power BI engine and desktop startup times, which indirectly speed up complex DAX evaluations and report refreshes. These enhancements improve responsiveness for analysts working on large semantic models and can make iterative development less frustrating. Even so, raw engine improvements do not eliminate the need for smart model design, because poorly structured calculations or inefficient relationships still create bottlenecks. In short, tool improvements compound with good practice rather than replace it.

Best practices and balancing tradeoffs

The presenter reiterates widely recommended best practices such as simplifying overly complex DAX, optimizing underlying databases, and aligning model design to reporting needs to avoid performance traps. While these techniques generally boost performance, there are tradeoffs: for instance, denormalizing data can speed queries but may increase storage and maintenance overhead. Similarly, choosing Direct Lake for freshness may complicate query tuning compared with a fast in-memory model that requires scheduled refreshes. Therefore, teams must balance speed, cost, freshness, and maintainability based on their priorities and resources.

Challenges, considerations and newsroom takeaway

Finally, the video acknowledges that AI suggestions, storage modes, and engine upgrades introduce both opportunity and complexity for BI teams, so careful governance is essential to avoid performance regressions and unexpected costs. Moreover, testing remains critical: automated suggestions and new storage options should be validated across representative workloads before broad rollout. For newsroom readers and BI managers, the key takeaway is pragmatic optimism—Microsoft’s 2025 updates provide useful tools that streamline many tasks, yet success depends on thoughtful adoption and continued attention to model design. In conclusion, the video by How to Power BI offers a clear overview of current trends while reminding practitioners that tradeoffs and verification remain central to delivering reliable, fast analytics.

Keywords

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