Red Hat OpenShift | Datadog

Red Hat OpenShift

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Run an efficient OpenShift environment

Red Hat OpenShift helps enterprise organizations build a stable, secure platform on top of Kubernetes by extending its security and development workflow capabilities. However, these capabilities can make monitoring and understanding application performance more challenging. Datadog simplifies monitoring in OpenShift environments with a Red Hat-certified Operator that enables teams to deploy the Agent using a single Kubernetes manifest. That way, Datadog automatically scales with Kubernetes, so any team can monitor its applications regardless of the number of pods and nodes they are running.

Reduce time to resolution with complete visibility into OpenShift

Organizations need visibility into every layer of their OpenShift environments in order to reduce troubleshooting time and confidently resolve performance issues. Datadog's built-in OpenShift and Kubernetes integrations allow teams to monitor all of their application components side by side and easily pivot between critical application metrics, logs, and traces. Teams can use Datadog's out-of-the-box Kubernetes dashboards to keep track of the nodes, deployments, and pods deployed to their OpenShift environment in one place.

And with more than 400 integrations, Datadog can easily support the other technologies that organizations rely on in their OpenShift environments. Datadog automatically applies tags to all telemetry data, so teams can slice and dice and correlate performance data from OpenShift with the rest of their infrastructure.

Proactive monitoring for complex infrastructure

Applications deployed in OpenShift environments rely on a diverse range of supporting technologies and services. The ability to anticipate potential issues in these types of environments is critical to reducing incident frequency and the costs of resolving an outage or service disruption. Datadog helps teams proactively monitor their OpenShift ecosystem and resolve incidents before they significantly affect customers.

Machine learning-driven analysis tools like anomaly detection and forecasting enable teams to understand performance baselines and alert on any unusual changes in key metrics, such as a sudden drop in disk space on a node. Datadog automatically retains metric data for 15 months, so organizations can perform long-term historical analysis and forecast requirements for optimizing their environments.