How Do You Optimize KQL Queries for High-Volume Real-Time Analytics?
As real-time data volumes grow, keeping analytics queries responsive while maintaining low-latency insights can become challenging in Microsoft Fabric Real-Time Intelligence. I'm interested in learning how teams optimize KQL queries when handling continuous streams of high-volume events. A few areas I'd like to explore: Which KQL optimization techniques have the biggest impact on query performance? How do you manage high-cardinality columns and large event datasets? When should you use materialized views instead of querying raw event data? What strategies help maintain responsive dashboards during heavy ingestion workloads? I'd love to hear practical experiences, optimization techniques, and recommendations from teams working with Microsoft Fabric Real-Time Intelligence.How Are Teams Turning Real-Time Data into Automated Business Actions?
Real-time data is valuable, but its real impact comes from how quickly organizations can turn incoming events into meaningful business actions. With platforms like Microsoft Fabric Real-Time Intelligence, teams can monitor streaming data, identify important events, configure alerts, and respond to changing conditions. However, connecting these insights with actual business workflows remains a challenge. For example, organizations could automate operational notifications, trigger follow-up processes, route tasks to the right teams, and use AI-driven workflows to reduce manual intervention. I'm interested in understanding how different teams are approaching this. How are you using real-time events to trigger automated workflows? What challenges do you face with event detection, alert reliability, and downstream integrations? Are you combining real-time analytics with AI agents to support faster business decisions? For organizations exploring this broader approach, AI business automation solutions can help connect intelligent automation with everyday business operations. Would love to hear about practical implementations and lessons learned.
Allow multi-column conditions/output in Power BI Activator rules (Power BI visual-based alerts)
Description: When I create an Activator alert from a Power BI report visual, the rule can only evaluate the single column or measure it was created from, and the alert can only reference that same value. This blocks common governance and monitoring scenarios. Example scenario: I wanted an alert for Dev workspaces that sit on a billable (Premium/Fabric) capacity. That needs a condition on both Workspace Name (contains “Dev”) and Capacity/Billing type (not blank). Activator can’t combine the two in one rule, so I had to build the alert in Power Automate with a DAX query instead. Limitations: A condition can use only one column or measure, with no AND/OR across several attributes. The alert message or action can include only that same column. I can’t add related details like workspace name, dataset link, or owner. There’s no way to run a DAX query as the rule logic. Requested improvements: Conditions across multiple columns/measures (AND/OR). Selectable output fields, so the Teams or email action can include several columns, and dynamic links. Optionally, the ability to define the trigger with a DAX query, as the Power Automate “Run a query against a dataset” action does.Working with Microsoft Fabric Support - A Collaborative Approach
At Microsoft Fabric Support, our goal is simple: help you resolve issues as quickly and smoothly as possible. Just like the recommendations shared in the Microsoft Fabric Community’s guidance on getting questions answered effectively, providing clear and complete information upfront helps everyone move faster and avoid unnecessary back-and-forth. Support is most effective when it’s a collaborative process. You bring the knowledge of your environment and workloads, and we bring deep platform expertise and diagnostics to help investigate the issue together. Help Us Help You When opening a support case, please include as much of the following information as possible: Issue Details Clear description of the problem Expected behavior vs. actual behavior Is the issue intermittent or consistently reproducible? Approximate timeframe of when the issue occurred (UTC preferred) Error Information Please include: Full error message text Screenshots (highly recommended) Activity IDs / Correlation IDs if available Even small details can significantly speed up investigation. Environment Information That Helps Investigation Depending on the Fabric workload being used, please provide the relevant item IDs and environment details. Workspace Information Workspace ID / Workspace URL To find this: Open the Fabric workspace Copy the URL from your browser Example: ".. https://app.fabric.microsoft.com/groups/<WorkspaceID>/… …" The value after /groups/ is the Workspace ID. Screenshot example above showcases the WorkspaceID and EventhouseID. Fabric Item IDs Please provide the item ID related to the affected workload, such as: EventhouseID ActivatorID LakehouseID WarehouseID SemanticModelID EventstreamID PipelineID These IDs help us locate telemetry and backend diagnostics more efficiently. You can typically find these: In the browser URL while inside the Fabric item Within item settings/details pages From the Fabric portal navigation pane Additional Environment Details Please also include: "… WorkspaceID: Capacity Name: Region …" Example: "… WorkspaceID: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx Capacity Name: Fabric-Prod-EastUS Region: East US …" This information helps us identify: Capacity-level issues Regional service impact Configuration-specific behavior Why This Matters Microsoft Fabric is a distributed cloud platform, and troubleshooting often depends on correlating logs, telemetry, timestamps, and resource identifiers together. Providing complete details upfront helps: Reduce delays Avoid repeated clarification requests Accelerate root cause analysis Create a smoother support experience for everyone involved We’re here to partner with you throughout the process, and we truly appreciate your collaboration in helping us investigate issues effectively. We look forward to working with you!
