Hi
I want to understand the billing options and cost optimization considerations for workloads where we wish to “decouple” Power BI and Lake House / ETL to separate “capacities”.
From my understanding, I am planning to follow the below mentioned steps:
- Have ETL / Spark workloads in one workspace A with Capacity A (say, capacity = F4, F8)
- Have Power BI workloads in another workspace B with Capacity B (say, capacity = A1)
- Foster a connection b/w workspace A and workspace B so that I can save costs as much as possible (with or without One-Lake Shortcuts)
- Spark workloads are on-demand, used for limited period of time per day
- Power BI workloads are on-demand as well
- Note - When the capacity is not used, it must be paused to save costs
- Also, where should the Power Bi Semantic Model reside (Capacity A or Capacity B)?
Referring to the example shared in the following link Fabric Capacity and One Lake Consumption, I want to understand how I can:
- Foster cross-workspace connectivities
- Save costs as much as possible (leveraging pause-resume capabilities for Fabric)
- What does it mean when we say - “storage is billed to the capacity” like in the above example “the data is still stored in Capacity1, the data stored is billed to Capacity1.”?
Also, need a clarification for the following line “As Capacity2 accesses the data, the compute or transaction cost for this read operation consumes CU from Capacity2. The storage continues to be billed to Capacity1.”
- Will the storage billing be at the rate Fabric Capacity consumed at CU / per hour is it normal ADLS / blobs storage pricing (which is nominal)
