Dual SKU for splitting workload

Hello

The SKU usage is based on what capacity is using which services.

if you query a Lakehouse in SKU1 from a different capacity, SKU2, then the read from the OneLake tables will be paid by the Lakehouse capacity, SKU1, and the write will be paid by SKU2.

The same thing goes for any other workload.

reads and query data processing is paid by the "giving" SKU, and the live wrangling and storage is paid by the "asking" SKU.

i recently saw a post about Microsoft working on something around shared Copilot capacity, to use across SKUs.

you can also define a minimum available capacity unit amount for each workspace, to guarantee the correct compute power when needed.

cheers

I can see from my workloads in my demo environments that when I read from SKU1 with a Notebook from SKU2, then SKU1 pays for the read, SKU2 pays for the spark compute and write.

It also makes sense, as the spark compute is configured in the local sku (in this case SKU2) and not on the SKU1 area. My compute is writing to SKU2, which is why I mentioned the cost for that in my above scenario 🙂

I can't find the documentation for the reserved max compute, but the screenshot in the portal is like this: