Visualization is an integral aspect of data analysis and interpretation. In Cloud Observability, it's not just about viewing data, but understanding the narrative it conveys. Depending on the telemetry data type and the insights you seek, you can deploy a variety of chart types to represent and discern the information effectively. Charts are available across Dashboards and Notebooks.
Telemetry Selection: Laying the Foundation
The first step is selecting the telemetry type for the chart:
- Metric data: Measures and provides numerical data, often over a period.
- Span data: Represents individual operations within a distributed trace.
- Log data: Chronological records from systems, detailing events or measurements.
Based on your selection, you'll employ different query tools to extract and present the data.
Functions: Enhancing Clarity
Once telemetry data is selected, various functions can refine the display:
- Group by: Cluster data based on shared attributes (e.g. customer_id, server, region, etc...), revealing patterns or trends across similar data points.
- Filter: Screen out unnecessary information, allowing for a more focused view on data subsets.
Selecting the Right Visualization
With data prepped, you can choose a fitting chart type to represent it. Here's a quick guide:
 Area chart in Cloud Observability |
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 Bar chart in Cloud Observability |
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 Heatmap chart in Cloud Observability |
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 Scatterplot chart in Cloud Observability |
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 Ordered list chart in Cloud Observability |
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 Dependency diagram in Cloud Observability |
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Crafting meaningful stories
Selecting the right visualization is about more than aesthetics; it's about ensuring the data tells a meaningful story. By aligning telemetry type, employing refining functions, and choosing the best-fit chart type, Cloud Observability enables you to transform raw data into comprehensible, actionable insights. As you explore, remember that each visualization method offers a unique lens through which to understand your data.
