notebooks

To create a Datadog notebook for performance metrics, use the notebooks skill to define a name and add content cells like a timeseries graph for specific metrics such as avg:system.cpu.user{*}. You can then share the notebook with your team for collaborative investigation and reporting.

Datadog notebooks support multiple data visualization cell types including markdown, timeseries, toplist, heatmap, distribution, and log stream cells. These allow you to document context and visualize system metrics, performance data, and log streams within a single notebook.

Yes, managing Datadog notebooks through this approach requires the pup CLI tool and access to Datadog API keys. These prerequisites allow the scripts to authenticate and interact with the Datadog Notebooks API for creating and updating notebook content.

Yes, you can collaborate on shared Datadog notebooks by tracking authors, sharing notebooks with team members, and setting global time ranges or applying cell time overrides to focus investigations on specific periods.

Automating Datadog notebook reporting is best achieved by using scripts that manage notebooks through the Notebooks API. This allows you to programmatically create, update, and delete notebooks containing timeseries and log stream cells for consistent metric reporting.