Data from monitoring tools like Datadog are useful for developers to help them understand whether the code they've deployed is healthy or needs to be fixed or rolled back, or when there is an incident to investigate.
As a deployment mission control, Sleuth helps developers see metrics data from a developer-centric point of view - by deployment - and interpret such data for them.
Datadog is an impact source in Sleuth, meaning it's one of many sources of data Sleuth can use to track the impact of deployments. We consider Datadog a metric tracker, just like New Relic or AWS CloudWatch (we support integrations with those, too).
Sleuth collects and organizes data from Datadog for use by developers. Examples of such data might include the average response times of your APIs, average CPU usage, or the percentage of notifications sent within a five-minute SLA. Sleuth then uses this data to make smart inferences about the health of a deployment.
Read the docs on how to integrate Datadog with Sleuth.
When you install the Datadog integration in Sleuth, Sleuth immediately begins pulling in the Datadog metrics of your choosing and tying them to their related deployments. If you're doing network monitoring, process monitoring, any kind of performance monitoring in Datadog already, all that data can be made available in Sleuth.
This gives you a big-picture perspective of your project’s health as measured across past and current deployments, and allows Sleuth to detect unusual activities even before incidents occur and alert the authors of the deployed code.
You can take advantage of Datadog's broad integration support in Sleuth. If you have custom metrics you're monitoring in Datadog, or metrics from any of the hundreds of technologies they support in dashboards you've built, you can track those metrics in Sleuth.
Datadog integration is one of our most popular integrations. Check out the integration announcement on the Datadog blog, or contact us if you have any questions!