Google Kubernetes Engine (GKE) is a managed Kubernetes service that enables users to deploy and orchestrate containerized applications on Google’s infrastructure. Datadog’s GKE integration, when paired with our Kubernetes integration, has always provided deep visibility into the health and performance of your clusters at the node, pod, container, and application levels.

Kubecon 2022 just concluded with plenty of exciting announcements, including our new open-source project, Helm-Dashboard, but we’ve got some more news to share on top of that.

The KubeCon event is a major cloud-native gathering thousands of people and hundreds of vendors for 3 days. Technology enthusiasts and adopters from leading cloud-native and open-source communities gather and discuss innovative ideas at KubeCon. It provides a forum where you can exchange relevant information and insights on the latest trends in Kubernetes and the container world.

Data is a modern company’s greatest asset, if used effectively. After all, in our always-connected economy, the most valuable business applications are data-driven. Customers expect real-time interactions powered by millions of end-points and massive amounts of data. To remain competitive in the market, organizations are adopting fast data applications to create new business models and transform industries, and Kubernetes is increasing the velocity with which they can be deployed.

As an operations engineer (SRE, IT manager, DevOps), you’re always struggling with how to manage technology and data sprawl. Kubernetes is becoming increasingly pervasive and a majority of these deployments will be in Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), or Azure Kubernetes Service (AKS). Some of you may be on a single cloud while others will have the added burden of managing clusters on multiple Kubernetes cloud services.

Cloud-native applications offer a lot of flexibility and scalability, but to leverage these advantages, we must create and deploy a suitable environment that will enable cloud-native applications to work their magic. Managed services, self-managed services, and bare metal are three primary categories of Kubernetes deployment in a cloud environment.

Containers have been the buzz among developers in recent years with the adoption of cloud-native orchestration tools like Kubernetes and DevOps workflows centered around containers. At the same time, virtual machines (VMs) still power many enterprise workloads, whether they’re running in a public cloud provider like Azure or an on-premises data center running VMware. In one of my early jobs, we built a private cloud—in 2012. This was a ground-breaking project at the time.

Users live in the sunlit world of what they believe to be reality. But, there is, unseen by most, an underworld. A place that is just as real, but not as brightly lit. The Kernel Parameter side (apologies to George Romero). Kernel parameters aren’t really that scary in actuality, but they can be a dark and cobweb-filled corner of the Linux world. Kernel parameters are the means by which we can pass parameters to the Linux (or Unix-like) kernel to control how that it behaves.

It can be a challenge to measure costs within a SaaS company. While a business with physical inventory can count the number of items sitting on the shelf and the money needed to create, store, and ship those items, operating within the cloud means SaaS companies have to measure their costs through a layer of abstraction. The number of users a given product supports and the resources needed to keep that product up and running could change by the minute.

Over the last few years, containers have been one of the hottest topics when it comes to radically changing the way we develop, operate, and maintain our applications. And it’s no coincidence the rise of containers has occurred right alongside the rise of the cloud and the DevOps movement. This shift in development and operations has brought new challenges as container technologies have evolved.