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Best Practices for Managing Microsoft Fabric Capacities

  • Michael Hofer
  • Oct 15, 2024
  • 3 min read

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The second part of the session at the Microsoft Fabric European Community Conference 2024 focused on best practices for managing Microsoft Fabric Capacities. Lukasz Pawlowski and Pat Mahoney shared valuable tips, tricks, and strategies to help businesses get the most out of their compute resources. Below are the key takeaways to ensure you optimize your Fabric Capacity usage.



  1. Understand Your Workload Needs


Before scaling up or down, it’s essential to analyze your workload patterns. Start by identifying peak and low usage times and plan your capacity accordingly. The session emphasized the importance of testing workloads in a trial capacity to gather metrics on actual compute usage.



2. Utilize Bursting and Smoothing Effectively


One of the most effective strategies to get the most from your capacity is leveraging bursting and smoothing:


  • Bursting: Helps during high-demand periods by utilizing unused CUs.

  • Smoothing: Spreads the compute cost over time, ensuring large workloads don’t monopolize compute power.


These features can help businesses avoid overprovisioning capacity for short-term spikes, allowing them to size for average usage rather than peak.



3. Take Advantage of Resizing and Pausing


The resize feature allows businesses to scale up during periods of high usage and scale downwhen the demand decreases. Pausing is another useful tactic, particularly for organizationsthat don’t require constant compute power:

  • Resize: Scale between smaller (F2, F4) and larger (F64, F128) capacities based oncurrent needs.

  • Pause/resume: Pause your capacity when not in use to avoid unnecessary costs.



4. Monitor and Optimize Capacity Usage


The Metrics App within Microsoft Fabric is a critical tool for monitoring capacity performance. Here’s how to use it effectively:


  • Check the ratio of interactive to background workloads. High background activitymay indicate that large dataflows or notebooks are consuming too much compute.

  • Use the throttling tab to identify periods where capacity limits were exceeded. If yousee frequent spikes, consider resizing or optimizing workloads.

  • Drill down into specific time points to see which jobs are consuming the most compute,and adjust accordingly.



5. Leverage Multi-Capacity Strategies


For larger organizations, splitting workloads across multiple capacities can prevent onecapacity from being overwhelmed:


  • Scale out: Distribute different workloads (e.g., Power BI, dataflows) across multiplecapacities.

  • Optimize first: Before purchasing additional capacity, review workload efficiency toensure best practices are followed.

  • Use tryout and timeout capacities: If you're unsure about a new workload's impact,allocate it to a "tryout capacity" first. If a workload becomes problematic, move it to atimeout capacity for isolation and resolution.



6. Best Practices for Throttling


Throttling occurs when a capacity is overused, but there are several strategies to prevent or resolve it:


  • Monitor workloads: Regularly check for overages and optimize workloads to reduce unnecessary compute consumption.

  • Resize or pause: Temporarily scale up or pause your capacity to reset the throttling limits and ensure critical jobs continue to run smoothly.



7. Plan for Capacity Growth


As your organization grows and takes on more workloads, make sure to regularly review your capacity usage. The upcoming Cross-Capacity View feature will provide a comprehensive overview of all your capacities, helping you identify underutilized resources that can be repurposed or consolidated.



Conclusion


Managing Microsoft Fabric Capacities efficiently requires a proactive approach. By monitoring usage, leveraging features like bursting and resizing, and optimizing workloads, businesses can ensure they are maximizing their compute resources without overspending. With the right strategies, you can make sure your organization gets the most capacity for its money.

 
 

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