Direct Lake

[binitafulpagare]

Hi everyone,

Direct Lake is often described as one of Microsoft's most significant innovations for Power BI and Fabric.

For those using Direct Lake in production:

How has it performed compared to Import mode or DirectQuery?
Have you encountered any limitations or scenarios where Direct Lake wasn't the best choice?
What best practices would you recommend before implementing it?

I'd love to hear about your real-world experiences rather than benchmark results.

Thanks!

[v-kathullac · Accepted solution]

Hi @binitafulpagare  , 

Below are the few points that resolves your issue.

• Organizations still choose Import mode when they need highly optimized performance, complex DAX support, or advanced semantic model capabilities.
• Import mode is suitable for smaller datasets, stable reporting requirements, and scenarios where scheduled refreshes are acceptable.
• Direct Lake is preferred for large-scale analytics where data is already in OneLake and near real-time access with minimal refresh management is required.
• Enterprise decisions are mainly based on Data volume and growth,User concurrency,Report complexity,Refresh requirements,Capacity utilization.
• Many organizations use a hybrid approach: Direct Lake for large-scale data workloads and Import mode for curated or highly optimized reporting models.
•  

Thanks,

Chaithanya.

[binitafulpagare]

Hi @v-kathullac,

Thank you for the detailed explanation and for clearly outlining the scenarios where Import mode and Direct Lake are most appropriate.

I appreciate your comparison of the two approaches and the emphasis on factors such as data volume, user concurrency, report complexity, refresh requirements, and capacity utilization when selecting the right storage mode. The point about many organizations adopting a hybrid approach by combining Direct Lake for large-scale analytics with Import mode for curated, highly optimized reporting is particularly insightful.

Thank you again for sharing these practical recommendations. They provide valuable guidance for understanding how organizations evaluate and implement different semantic model strategies in Microsoft Fabric.

[Prince0011]

Hi @User,

Direct Lake has been a significant addition to Microsoft Fabric, especially for organizations looking to combine the performance of Import mode with the freshness of DirectQuery.

From what many teams have shared, here are some common observations:

• Direct Lake delivers excellent query performance for large semantic models without requiring scheduled imports.

• It works best when your data is stored in OneLake and follows a well-designed star schema.

• Compared to DirectQuery, users often experience lower latency and a more interactive reporting experience.

• Compared to Import mode, it reduces the need for refresh operations while keeping data more up to date.

Some considerations before adopting Direct Lake:

• Optimize your Lakehouse tables (partitioning, file sizes, and Delta table maintenance) to achieve the best performance.

• Design an efficient semantic model and avoid unnecessary complexity in DAX and relationships.

• Validate whether all required Power BI features for your solution are supported in Direct Lake mode, as some scenarios may still require Import or DirectQuery.

• Monitor capacity usage and query performance regularly, especially as your data volume and user concurrency grow.

For more information:

• Direct Lake overview: https://learn.microsoft.com/fabric/get-started/direct-lake-overview

• Direct Lake in Power BI: https://learn.microsoft.com/power-bi/enterprise/directlake-overview

• Microsoft Fabric documentation: https://learn.microsoft.com/fabric/

I'd also be interested to hear from teams using Direct Lake in production. What has your experience been regarding performance, scalability, and any limitations you've encountered compared to Import mode or DirectQuery?

[binitafulpagare]

Hi @Prince0011,

Thank you for the detailed explanation and for sharing the official Microsoft documentation.

Your comparison between Direct Lake, Import mode, and DirectQuery clearly highlights the strengths of Direct Lake, particularly its ability to deliver high query performance while minimizing the need for scheduled refreshes. The recommendations around optimizing Delta tables, maintaining a star schema, and monitoring capacity are especially valuable for production deployments.

I have one follow-up question. As data volumes and concurrent users continue to grow, are there scenarios where organizations still prefer Import mode over Direct Lake for specific semantic models or reporting workloads? Understanding the decision criteria used in real-world enterprise environments would be very helpful.

I'd also be interested in hearing from community members who have deployed Direct Lake at scale and learning what best practices or challenges they've encountered.

Thank you again for sharing these practical insights.