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Real-Time Dashboards help you transform query results into interactive visuals that support monitoring, troubleshooting, and decision-making. Whether you're tracking system health, analyzing trends, monitoring business metrics, or investigating operational issues, you can create a dashboard that combines multiple visual types into a single experience. Real-Time Dashboards support a wide range of charts, tables, maps, KPI indicators, and dashboard-specific visuals.
This article provides an overview of the visual capabilities available in Real-Time Dashboards and how you can customize and organize them to tell your data story effectively.
Choose the right visual for your data
Most dashboard visual tiles combine a Kusto Query Language (KQL) query with a visual representation of the query results. Real-Time Dashboards can also display referenced Fabric items, such as Fabric Maps.
Different visual types are suited for different types of data and analysis needs. For example:
- Use time-based charts to monitor trends and detect changes over time.
- Use bar, column, pie, and funnel charts to compare categories and distributions.
- Use tables to inspect detailed records.
- Use KPI, Stat, and Multi Stat visuals to highlight important metrics.
- Use the built-in Map visual to display geographic data returned by a tile's KQL query.
- Use a Fabric Map visual to display a separately authored, multilayer Fabric map.
- Use heatmaps to identify concentration and patterns across dimensions.
For a complete list of supported visual types and their use cases, see Supported visuals in Real-Time Dashboards.
Build and edit dashboard visuals
Dashboard authors can add new tiles and modify existing tiles. For query-backed visual tiles, authors can also update the underlying query and configure how the query results are displayed. In editing mode, you can:
- Add new tiles.
- Change the visual type of a query-backed tile.
- Edit the underlying KQL query.
- Configure visual-specific settings.
- Update formatting and display options.
Real-Time Dashboards also support Copilot-assisted authoring for query-backed visuals, allowing you to create and modify visuals using natural language prompts.
Organize your dashboard layout
A dashboard is a collection of visual tiles that can be arranged to support the way users consume information. After adding visuals, you can organize them into a meaningful layout by:
- Drag-and-drop to move tiles to different positions.
- Resize tiles to emphasize important information.
- Add markdown content for instructions, context, or documentation.
Effective layouts help viewers quickly identify the most important metrics and understand relationships between different visualizations.
Customize visual appearance
Most visual types include formatting options that control how data is displayed. Depending on the visual, you can customize:
- Colors and color palettes.
- Axis labels and scales.
- Legends and series presentation.
- Data aggregation and grouping.
- Reference lines and target indicators.
- Layout and display orientation.
- Time ranges and zoom behavior.
- Data series colors for consistent visual storytelling.
These settings help you tailor visuals to your audience and make important patterns easier to identify.
For detailed customization options, see Customize dashboard visuals.
Highlight important data with conditional formatting
Conditional formatting helps viewers focus on values that require attention. You can apply rules that change colors, display icons, or add tags when specific conditions are met. Depending on the visual type, conditional formatting can:
- Highlight critical values.
- Emphasize warnings and thresholds.
- Color individual cells or entire rows.
- Apply color gradients based on data values.
- Change KPI states based on configured thresholds.
For step-by-step instructions, see Apply conditional formatting in Real-Time Dashboard visuals.
Specialized visual experiences
Some scenarios benefit from dashboard-specific visuals and capabilities, including:
- KPI visuals for operational monitoring and threshold tracking.
- Time Series visuals for analyzing multiple measures over time.
- Markdown visuals for contextual information and documentation.
- Fabric Map visuals for displaying separately authored, multilayer Fabric maps.
Each visual type includes its own configuration options and best practices.