Sir please make a video on topic that where can we analyze the capacity consumption like we want to see which workload or item is taking how much capacity or compute...
Hi Amit, thank you for the tutorial! I'm interested in setting limits in Fabric. Can operations that exceed a cost limit be automatically shut down? For example, if a company accidentally leaves a process running over the weekend, resulting in charges of over 30,000 Euros, would implementing a kill-cost limit of, let's say, 1,000 Euros have prevented this? I'd appreciate any insights you can provide. Thank you!
I would like to understand if the cost issue happens with Fabric. Because as of now, we are limited by capacity, so P1 will incur P1 costs and transfer costs. Please provide more details. In the meantime, I will check and update if you can stop capacity based on cost.
Lets say my organization is Having SQL (on prem), ASQL , sharepoint ,Adf and databricks workloads along with power bi license for say 10K users. I now wish to move all to fabric. Can you help me to understand what parameters I would be looking to choose the Fabric capacities for above mentioned workloads?
In case you are using Power BI, My assumption is you are using P capacity. You can start using fabric with that and later you can migrate to Fabric Capacity. We have a Warehouse that supports SQL, We also have notebooks(ADB), and We have a Data Factory in Fabric. Start with Trial. Check opportunities to lift and shift You need to plan for the migration of the rest of the content.
"Hello, in my organization, we have one Power BI developer and 30 other users who have been added from our Azure ID. Currently, we are using the Fabric trial and now I want to purchase Fabric. Which pricing plan would you suggest as being the most affordable?"
@AmitChandak But sir, the F64 costs 1.9 Lakh for 200 hours, which is not feasible for our organization. Also, we don't have a workload that requires 3 workspaces and a maximum PBIX file size of 15 MB. Can we add users to view reports in F8 or F2, or is there another option you would recommend?
I think you can start with F2 and later upgrade to F4 if needed. F2 should be good if the size is a few MBs. You can opt for pay-as-you-go to start with.
We usually recommend F64 capacity because of the various benefits it offers, such as handling larger table sizes and data model sizes. If you are working with small data, you can opt for a smaller capacity. If you share more details about your data and usage, I can provide a better suggestion. You can ping me on LinkedIn- www.linkedin.com/in/amitchandak78/
If you are using a Fabric for a full day that includes data loading, Spark work, Power BI usages, etc, you should go for reserved capacity. Usually reserved capacity is 30-40% cheaper, which means if you are using the capacity for more than 16 hours, reserved capacity is cheaper.
Sir please make a video on topic that where can we analyze the capacity consumption like we want to see which workload or item is taking how much capacity or compute...
Hi Amit, thank you for the tutorial! I'm interested in setting limits in Fabric. Can operations that exceed a cost limit be automatically shut down? For example, if a company accidentally leaves a process running over the weekend, resulting in charges of over 30,000 Euros, would implementing a kill-cost limit of, let's say, 1,000 Euros have prevented this? I'd appreciate any insights you can provide. Thank you!
I would like to understand if the cost issue happens with Fabric. Because as of now, we are limited by capacity, so P1 will incur P1 costs and transfer costs. Please provide more details. In the meantime, I will check and update if you can stop capacity based on cost.
Lets say my organization is Having SQL (on prem), ASQL , sharepoint ,Adf and databricks workloads along with power bi license for say 10K users. I now wish to move all to fabric. Can you help me to understand what parameters I would be looking to choose the Fabric capacities for above mentioned workloads?
In case you are using Power BI, My assumption is you are using P capacity. You can start using fabric with that and later you can migrate to Fabric Capacity. We have a Warehouse that supports SQL, We also have notebooks(ADB), and We have a Data Factory in Fabric.
Start with Trial.
Check opportunities to lift and shift
You need to plan for the migration of the rest of the content.
@@AmitChandak Thanks!
What is one lake BCDR?
BCDR means Business Continuity and Disaster Recovery
"Hello, in my organization, we have one Power BI developer and 30 other users who have been added from our Azure ID. Currently, we are using the Fabric trial and now I want to purchase Fabric. Which pricing plan would you suggest as being the most affordable?"
Depending on the workload, the F8 or F16 can be a good choice. However, I always recommend the F64 if it is within budget.
@AmitChandak But sir, the F64 costs 1.9 Lakh for 200 hours, which is not feasible for our organization. Also, we don't have a workload that requires 3 workspaces and a maximum PBIX file size of 15 MB. Can we add users to view reports in F8 or F2, or is there another option you would recommend?
I think you can start with F2 and later upgrade to F4 if needed. F2 should be good if the size is a few MBs. You can opt for pay-as-you-go to start with.
which capacity is recommended for Machine learning in my business, i have already nearly exhausted my trial period.
We usually recommend F64 capacity because of the various benefits it offers, such as handling larger table sizes and data model sizes. If you are working with small data, you can opt for a smaller capacity. If you share more details about your data and usage, I can provide a better suggestion. You can ping me on LinkedIn- www.linkedin.com/in/amitchandak78/
how to decide need to purchase pay as you go or reserved Capacity?
If you are using a Fabric for a full day that includes data loading, Spark work, Power BI usages, etc, you should go for reserved capacity. Usually reserved capacity is 30-40% cheaper, which means if you are using the capacity for more than 16 hours, reserved capacity is cheaper.