Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has revolutionized how developers deploy, manage, and scale their applications. One of its key features is the ability to scale deployments seamlessly. This article explores various aspects of using the kubectl scale deployment command, including how to scale deployments up and down, scale all deployments in a namespace, managing replica sets, and more.

Earlier this year at Mobile World Congress (MWC) 2024 in Barcelona, Canonical announced the availability of Real-time Ubuntu on Amazon Elastic Kubernetes Services Anywhere (EKS Anywhere). With this technology enablement, a telecom operator can confidently run its Open Radio Access Network (RAN) software workloads on Amazon EKS Anywhere, thanks to the necessary support for ultra low-latency data processing with high reliability in Real-time Ubuntu.

Over the last few weeks I've been talking about the key differences between Amazon EKS and Cycle. If you happened to miss it and want to catch up before diving into this post you can check it out here: This post will round out the series by taking a look at how worker nodes are added to a cluster and the major differences between EKS and Cycle there.

DevOps is essential for engineering teams looking to enhance collaboration, speed up development cycles, and maintain high-quality software delivery. Building on our previous discussion of common DevOps mistakes startups make, let's move on to ten practical strategies small engineering teams can adopt to foster a robust DevOps culture.

Red Hat OpenShift is an open-source container platform that provides a complete environment for developers to create, launch, and manage applications in both cloud and on-premises settings, leveraging the potential of container technology. With increasing popularity in container orchestration, OpenShift's strong scalability and features attract developers and IT experts. Nonetheless, the intricacy and changing aspects of OpenShift setups demand a tailored strategy for monitoring and observation.

Kubernetes has several components that produce logs and events containing information on everything that has happened in a Kubernetes cluster. Keeping track of all this data becomes extremely challenging when you run Kubernetes at a very large scale. With so many components generating logs, organizations need a centralized place to see it all. But this is only half your problem. You also need to correlate logs coming from different components to draw the right conclusions and take effective actions.