Kubernetes is known to be the best container orchestration tool, but it can also add complexity to resource management, particularly as your clusters expand. Without proper monitoring, problems can rapidly worsen, resulting in subpar application performance, service interruptions, and higher expenses. In this blog, you will learn the key metrics for monitoring Kubernetes performance and how monitoring these can assist you in maintaining optimal performance in your environment.

Managing a complex IT environment with both on-premises data centers and multiple cloud platforms (Azure, AWS, Google Cloud) brings a unique set of challenges. Commvault’s cloud operations team, led by Ernie Costa, was well aware of the high-performance systems running on technologies like Hyper-V and NVMe storage. In these systems, even a second’s delay could mean missed opportunities to prevent incidents or optimize performance.

For IT teams seeking speed and agility, agentless monitoring offers a lightweight approach. This is particularly useful for Microsoft servers like Windows Nano Server, where resources may be constrained, or in environments where gaining approval for agent installations could be a hurdle. An agentless Microsoft monitoring tool is ideal if: However, there are limitations.

The SRE Report is now seven years old. I’ve had the honor and privilege of authoring it for the last five years. This 2025 version included working with some amazing individuals like Kurt Andersen and Denton Chikura. My heartfelt thanks go to them for shouldering the weight of what is both a labor of love and an often daunting, procrastination-inducing marathon of analysis.

Google Cloud Run is a fully managed platform that enables you to deploy and scale container-based serverless workloads. Cloud Run is built on top of Knative, an open source platform that extends Kubernetes with serverless capabilities like dynamic auto-scaling, routing, and event-driven functions. By using Cloud Run, developers can simply write and package their code as container images and deploy to Cloud Run—all without worrying about managing or maintaining any underlying infrastructure.

In Part 1 of this series, we looked at key Cloud Run metrics you can monitor to ensure the reliability and performance of your serverless containerized workloads. We’ll now explore how you can access those metrics within Cloud Run and Google’s dedicated observability tool, Cloud Monitoring. We’ll also look at several ways you can view and explore logs and traces in the Cloud Run UI and Google Cloud CLI.

In part 1 of this series, we introduced the key Cloud Run metrics you should be monitoring to ensure that your serverless containerized applications are reliable and can maintain optimal performance. In part 2, we walked through a couple of Google Cloud’s built-in monitoring tools that you can use to view those key metrics and check on the health, status, and performance of your serverless containers.

It's no secret that building a data-driven culture in a company is hard, but what is it exactly that makes this such a tricky endeavor? Contrary to popular belief, technology isn't the main hurdle. A recent survey reveals that only a quarter of respondents cite technological limitations as the primary obstacle to becoming data-driven.

Docker has transformed how we deploy, manage, and scale applications. As applications grow in complexity, the need for effective orchestration increases. This is where Docker Swarm comes into play. Docker’s native clustering and orchestration tool simplifies the management of multi-container applications. Together, Docker and Docker Swarm form a powerful combination for building and scaling modern, distributed systems.