topic Re: Compare Fabric DW and Azure SQL performance in Data Warehouse

Compare Fabric DW and Azure SQL performance

MichelleTW729 — Wed, 04 Sep 2024 14:07:46 GMT

I have a project User need DW and Power BI , we try promote Fabric service , but also need compare ADF+AzureSQL which will better ? any compare table about Fabric capacity (F8 ) would near Azure SQL P1 or S3/S4 ?

Re: Compare Fabric DW and Azure SQL performance

AndyDDC — Wed, 04 Sep 2024 16:28:56 GMT

Hi MichelleTW729 in terms of directly mapping compute power between Fabric Warehouse and Azure SQL Database, we first look at the number of cores that will be used in the Fabric capacity. For each 1 CU, this gives us 0.5 vCores for the Warehouse service. So an F8 allows us to have 4 vCores. In Azure SQL DB, 100 DTU (I believe) maps to 1 vCore, 200 DTU = 2 vCores etc.

Bear in mind that if you have any other Fabric items running on the capacity, they will also consume compute. E.G. if you have a Warehouse plus Data Pipelines which all run on the F8 capacity, they will share the compute.

You will need to do your own testing in your environment to chek performance is adequate for your project.

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Re: Compare Fabric DW and Azure SQL performance

Anonymous — Thu, 05 Sep 2024 02:29:00 GMT

Hi MichelleTW729 ,

Thanks for the reply from AndyDDC .On the comparison of Fabric DW (F8) and Azure SQL P1 or S3/S4, he makes a good point.

A brief summary of how Fabric Data Warehouse compares to ADF+AzureSQL is as follows:

Fabric Data Warehouse: Better performance and scalability for large-scale analytics projects that require Data Warehouse and Power BI integration.

Azure SQL with ADF: Ideal for projects that require more data integration flexibility and handle smaller workloads.

Feature	Fabric Data Warehouse	Azure SQL P1/S3/S4
Data Storage	Delta lake format in OneLake	Relational storage
Query Performance	High performance, scalable	Robust
Integration with Power BI	Seamless integration	Flexible, needs more configuration
Suitable Workloads	Large-scale data warehousing	Small and medium-sized data processing

If you have any other questions please feel free to contact me.

Best Regards,
Yang
Community Support Team

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