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Hello everyone,
We are approaching new clients to implement Fabric, but we are facing a challenge to previously define an appropriate SKU.
Do you know which resource we can utilize to perform these CUs calculations? Something that we can use to convert from load amounts (MB, GB, TB) to CUs?
I consider that in these cases the trial and the capacity app are not an option, because they are big implementations, and setting up the trial environment will mean several efforts.
Any additional resources to perform the analysis / estimations?
Bests,
Solved! Go to Solution.
To predefine an appropriate Microsoft Fabric SKU, estimate Capacity Units (CUs) by modeling workloads based on data volume, operation types, and usage patterns.
While Microsoft Fabric doesn’t provide a direct formula to convert raw data sizes (MB, GB, TB) to CUs due to the variability of workloads , you can estimate CU requirements by modeling workloads based on data volume, operation types, and expected usage patterns.
Identify the client's primary Fabric workloads (eg: Data Warehouse, Lakehouse, Power BI reports, Dataflows, Spark notebooks) then estimate data volumes(e.g., 100 GB for a Lakehouse, 1 TB for a Data Warehouse), and determine the frequency and type of operations(e.g., daily ingestion, hourly queries, interactive reporting).
Ex: A client plans to ingest 500 GB daily into a Lakehouse, run 100 SQL queries per hour on a 2 TB Data Warehouse, and generate 50 Power BI reports viewed by 200 users daily.
Microsoft Fabric measures CU consumption in CU-seconds, where CUs represent compute resources (CPU, memory, I/O). Each workload consumes CUs differently based on data size and operation complexity
Workload-Specific CU Consumption Rates:
For instance:
Estimate CU Usage per Workload:
Example Calculation:
Lakehouse Ingestion (500 GB/day):
Assume ingestion via Dataflow Gen2, consuming ~0.1 CU-seconds per MB.
500 GB = 500,000 MB → 500,000 × 0.1 = 50,000 CU-seconds/day.
Data Warehouse Queries (2 TB, 100 queries/hour):
Assume each query processes 10 GB (10,000 MB) and consumes 100 CU-seconds.
100 queries × 100 CU-seconds = 10,000 CU-seconds/hour → 240,000 CU-seconds/day (24 hours).
Power BI Reports (50 reports, 200 users):
Assume each report consumes 10 CU-seconds per view, with 200 users viewing 50 reports daily.
50 reports × 200 users × 10 CU-seconds = 100,000 CU-seconds/day.
Total Daily CU-seconds:
50,000 + 240,000 + 100,000 = 390,000 CU-seconds/day.
Convert to CU-hours: 390,000 ÷ 3,600 = ~108 CU-hours/day.
Map to SKU:
If this post is helpful, please mark it as the Accepted Solution.
Thank You!
Thank you for reaching out to the Microsoft Fabric Community Forum.
A special thanks to @lbendlin for sharing helpful insights.
Regarding the estimation of Microsoft Fabric capacity
Evaluate workloads, convert data load amounts using online calculators, monitor capacity consumption with the Fabric Capacity Metrics app, and optimize resources following capacity planning guidelines.
For more detailed guidance, please refer to the official Microsoft documentation:
Evaluate and optimize your Microsoft Fabric capacity - Microsoft Fabric | Microsoft Learn
Understand the metrics app compute page - Microsoft Fabric | Microsoft Learn
Save costs with Microsoft Fabric Capacity reservations - Microsoft Cost Management | Microsoft Learn...
If my response has resolved your query, please mark it as the Accepted Solution to assist others. Additionally, a 'Kudos' would be appreciated if you found my response helpful.
Thank You
Hi Karpurapu.
It doesn't respond my query.
Bests,
To predefine an appropriate Microsoft Fabric SKU, estimate Capacity Units (CUs) by modeling workloads based on data volume, operation types, and usage patterns.
While Microsoft Fabric doesn’t provide a direct formula to convert raw data sizes (MB, GB, TB) to CUs due to the variability of workloads , you can estimate CU requirements by modeling workloads based on data volume, operation types, and expected usage patterns.
Identify the client's primary Fabric workloads (eg: Data Warehouse, Lakehouse, Power BI reports, Dataflows, Spark notebooks) then estimate data volumes(e.g., 100 GB for a Lakehouse, 1 TB for a Data Warehouse), and determine the frequency and type of operations(e.g., daily ingestion, hourly queries, interactive reporting).
Ex: A client plans to ingest 500 GB daily into a Lakehouse, run 100 SQL queries per hour on a 2 TB Data Warehouse, and generate 50 Power BI reports viewed by 200 users daily.
Microsoft Fabric measures CU consumption in CU-seconds, where CUs represent compute resources (CPU, memory, I/O). Each workload consumes CUs differently based on data size and operation complexity
Workload-Specific CU Consumption Rates:
For instance:
Estimate CU Usage per Workload:
Example Calculation:
Lakehouse Ingestion (500 GB/day):
Assume ingestion via Dataflow Gen2, consuming ~0.1 CU-seconds per MB.
500 GB = 500,000 MB → 500,000 × 0.1 = 50,000 CU-seconds/day.
Data Warehouse Queries (2 TB, 100 queries/hour):
Assume each query processes 10 GB (10,000 MB) and consumes 100 CU-seconds.
100 queries × 100 CU-seconds = 10,000 CU-seconds/hour → 240,000 CU-seconds/day (24 hours).
Power BI Reports (50 reports, 200 users):
Assume each report consumes 10 CU-seconds per view, with 200 users viewing 50 reports daily.
50 reports × 200 users × 10 CU-seconds = 100,000 CU-seconds/day.
Total Daily CU-seconds:
50,000 + 240,000 + 100,000 = 390,000 CU-seconds/day.
Convert to CU-hours: 390,000 ÷ 3,600 = ~108 CU-hours/day.
Map to SKU:
If this post is helpful, please mark it as the Accepted Solution.
Thank You!
Amazing Karpurapu, it was the kind of guidance that I was looking for.
Best regards,
Juan
Thank you so much Ibendlin, I've reviewed this documentation previously, but it doesn't repond my question.
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