Operations | Monitoring | ITSM | DevOps | Cloud

Datadog

Monitor IBM DB2 with Datadog

IBM DB2 is a database management system that runs on a wide range of technologies, including Linux, UNIX, Windows, mainframes, and IBM Power Systems. You can use DB2 as a managed service in the cloud or deploy it in a cluster to provide high availability, making it suitable for a wide range of enterprise and customer-facing applications.

Java runtime monitoring with JVM metrics in Datadog APM

Whether you’re investigating memory leaks or debugging errors, Java Virtual Machine (JVM) runtime metrics provide detailed context for troubleshooting application performance issues. For example, if you see a spike in application latency, correlating request traces with Java runtime metrics can help you determine if the bottleneck is the JVM (e.g., inefficient garbage collection) or a code-level issue.

Integrate Akamai with Datadog to monitor CDN performance

Akamai is a leading provider of content delivery network solutions around the world, handling many millions of HTTP requests per second. By Akamai’s estimates, its CDN platform delivers 15 to 30 percent of global web traffic. If you’re using Akamai to accelerate and protect the delivery of content to your users, we are pleased to announce that you can now use Datadog to monitor the utilization and performance of your CDN.

Monitor CoreDNS with Datadog

CoreDNS is a DNS server that can also provide service discovery for microservice-based applications. It’s the default DNS server in Kubernetes, providing name resolution and service discovery for the services operating in the cluster. CoreDNS is easily customizable, so you can define how it should act on each request beyond simply executing a DNS lookup.

Canary releases with Azure Deployment Manager and Datadog

Canary releases are a powerful technique for updating large-scale production environments safely. The idea is simple: deploy the update to a subset of your environment, pause and monitor to ensure everything is healthy, and then deploy to the next subset. But implementing these staged releases can be challenging, as you’ll need to retool your deployment pipeline and build programmatic health checks to validate the success of each canary release.