Forum Discussion
Direct Lake vS Direct Query
Came across this, has some deep insights.
i personally find Direct Query more flexible, what do you all prefer ?
https://medium.com/@rishabh.gulati_94109/microsoft-fabric-direct-lake-is-fast-good-data-architecture-is-faster-340276de2e6b
Hi,
I understand why you prefer DirectQuery is very flexible, especially when you need live data or can't move everything into OneLake.
But here's the key difference I've noticed:
- Direct Lake reads directly from Delta tables in OneLake. Then all the transformations, aggregations and joins happens before the report runs, so Power BI just uses pre-optimized data. The report is fast, but requires more ETL work upfront.
- DirectQuery queries the source every single time – warehouse, SQL Server, etc. It's flexible, but the performance depends entirely on how complex your queries and DAX are.
The catch with DirectQuery? If you have:
- Complex joins
- Heavy aggregations
- Lots of concurrent users
Then your warehouse becomes the bottleneck. I've seen reports that were perfectly fine with DirectQuery suddenly to a loading failure when the data grew or more users are using it.
So for me, it comes down to:
- Direct Lake = more engineering work upfront, but better performance and scalability
- DirectQuery = faster to set up, but can hit performance walls if your queries get complex
Both have their place. I use DirectQuery for quick simple and operational dashboards, but for enterprise reporting with lots of users, the Direct Lake approach (with a good Gold-layer star schema) has been more reliable with precalculated columns.
5 Replies
- GilbertQ
Super User
Hi Sparkdata
I personally prefer direct lake and the reason for that is that you still have to build this task schema with facts and dimension tables which align to business intelligence or data engineering and insights into your data, especially if you start using the AI on top of it. Whilst having things in Directquery this is often not the case and it makes getting out. The data you need for AI and reporting a lot more difficult.
v-aatheequeCommunity Support
Hi Sparkdata
Have you had a chance to look through the responses shared earlier? If anything is still unclear, we’ll be happy to provide additional support.
Hi,
I understand why you prefer DirectQuery is very flexible, especially when you need live data or can't move everything into OneLake.
But here's the key difference I've noticed:
- Direct Lake reads directly from Delta tables in OneLake. Then all the transformations, aggregations and joins happens before the report runs, so Power BI just uses pre-optimized data. The report is fast, but requires more ETL work upfront.
- DirectQuery queries the source every single time – warehouse, SQL Server, etc. It's flexible, but the performance depends entirely on how complex your queries and DAX are.
The catch with DirectQuery? If you have:
- Complex joins
- Heavy aggregations
- Lots of concurrent users
Then your warehouse becomes the bottleneck. I've seen reports that were perfectly fine with DirectQuery suddenly to a loading failure when the data grew or more users are using it.
So for me, it comes down to:
- Direct Lake = more engineering work upfront, but better performance and scalability
- DirectQuery = faster to set up, but can hit performance walls if your queries get complex
Both have their place. I use DirectQuery for quick simple and operational dashboards, but for enterprise reporting with lots of users, the Direct Lake approach (with a good Gold-layer star schema) has been more reliable with precalculated columns.
- v-aatheeque
Hi Sparkdata
Following up to confirm if the earlier responses addressed your query. If not, please share your questions and we’ll assist further.
I think the article also summarizes the points shared in various answers.
