Imagine a symphony where every musician plays their part flawlessly, but without a conductor to guide the orchestra, the result is just a discordant mess. Now apply that image to the modern IT landscape, where development and operations teams work with remarkable autonomy, each expertly playing their part. Agile methodologies and DevOps practices have empowered teams to build and manage their services independently, resulting in an environment that accelerates innovation and development.

With the recent integration between SUSE and StackState, SUSE customers will benefit from the enhanced observability StackState offers for their applications running on SUSE’s diverse Kubernetes distributions. As businesses increasingly rely on Kubernetes, ensuring the stability and performance of applications becomes of great importance.

This is the final article of a three-part series. To start at the beginning, read Part 1: Benefiting from multi-cluster setups requires familiarity with common variations and Part 2: Exploring the facets of a multi-cluster observability strategy. As companies scale software production, they lean on Kubernetes as a crucial container orchestration platform for managing, deploying and ensuring software availability.

This is the second of a three-part blog series. Prior to reading this, be sure to check out Part 1, Benefiting from multi-cluster setups requires familiarity with common variations. In your Kubernetes journey, it's highly likely that you'll encounter the need to manage multiple clusters simultaneously.

In the world of modern Kubernetes, things have come a long way from the days of a single cluster handling one app. Now, it's common to see setups that span multiple clusters across different clouds. Initially, managing those clusters was a complicated operation with many moving parts. Using tools such as SUSE Rancher, RedHat OpenShift or AWS EKS, made managing multiple clusters somewhat easier.

For many developers and engineers, Kubernetes is the de facto choice for container orchestration. That’s primarily because of its efficiency in handling and scaling container workloads. However, the complexity of managing nodes in a Kubernetes cluster can cause recurring headaches for even the most experienced and skilled IT teams. This is where `kubectl cordon` comes into play.

As software engineers, we all know that troubleshooting often involves sifting through heaps of data points — scanning metrics, reading logs, checking resource status and analyzing events. We manually connect the dots, and if we're experienced enough, we might spot an issue that's about to become a problem. At StackState, we've faced these same challenges.

If you work with Kubernetes, you know that any number of issues can pose a serious threat to the stability and security of your deployments. One that's subtly damaging is configuration drift, which occurs when the actual state of how your system is set up — its configuration — strays from the way you defined. Configuration drift in Kubernetes can happen when people make changes manually, systems aren't synchronized properly or monitoring falls short.

Picture this: You're knee-deep in the intricacies of a complex Kubernetes deployment, dealing with a web of services and resources that seem like a tangled ball of string. Visualization feels like an impossible dream, and understanding the interactions between resources? Well, that's another story. Meanwhile, your inbox is overflowing with alert emails, your Slack is buzzing with queries from the business side, and all you really want to do is figure out where the glitch is. Stressful? You bet!

In today's cloud-native landscapes, observability is more than a buzzword; it's a critical element for software development teams looking to master the complexities of modern environments like Kubernetes. There’s a multi-faceted nature to observability with all its various levels and dimensions — from basic metrics to comprehensive business insights. It’s complex and can continue indefinitely…if you let it.