Optimizing CU Usage in Microsoft Fabric

Hi CReportify 

Please try these options:

Reduce Driver Cores
Your notebooks are currently using 8 driver cores, which may be excessive for the amount of data processed. Try reducing the number of cores to 4 or even 2, as this can significantly lower CU consumption without necessarily impacting performance for your data volumes.

Consider Pipelines
For repetitive API data fetching, pipelines might be more efficient than notebooks. Pipelines can:
• Better handle scheduling
• Provide built-in error handling and logging
• Potentially reduce overall CU usage

Storage: Lakehouse vs. Warehouse
For your current data volumes, a Lakehouse is likely more cost-effective than a Warehouse. Lakehouses are optimized for:
• Handling diverse data types
• Frequent small-scale reads/writes
• Machine learning workloads
However, if you anticipate significant growth in data volume or require complex SQL analytics, a Warehouse might become more suitable in the future

Capacity Management
The massive CU usage spikes after resuming capacity are likely due to the “smoothing” mechanism in Fabric. When you pause capacity, Fabric immediately charges for all scheduled background operations instead of spreading the cost over 24 hours. To mitigate this:
• Schedule pauses during naturally low-usage periods
• Implement autoscaling for more dynamic workloads

Additional Optimization Strategies
1. Batch Processing: Implement batching in your API calls to reduce the number of individual requests, potentially lowering CU usage.
2. Data Partitioning: Properly partition your Lakehouse data based on common query patterns to improve read performance.
3. Caching: Utilize Spark caching for frequently accessed datasets to reduce computation overhead.
4. Monitor and Analyze: Regularly use the Fabric Capacity Metrics app to identify high-consumption operations and optimize accordingly

Hope this helps.

please give kudos and mark this as solution if it resolves your query.

thanks