Kubernetes is known for its modularity, and its integration with cloud environments. Throughout its history, Kubernetes provided in-tree cloud provider integrations with most providers, allowing us to create cloud-related resources via API calls without requiring us to jump through hoops to deploy a cluster that utilizes the power of underlying networking infrastructure. However, this behavior will change with the release of Kubernetes v1.31, and right now is the best time to plan for it.

The Generative Artificial Intelligence (GenAI) innovations and advancements over the past 1.5 years have been unmatched. Gartner predicts that by 2026, more than 80% of enterprises will have deployed GenAI-enabled applications in production environments and/or used GenAI application programming interfaces or models. This is up from less than 5% in 2023.

Service mesh is a tool for adding observability, security, and traffic management capabilities at the application layer. A service mesh is intended to help developers and site reliability engineers (SREs) with service-to-service communication within Kubernetes clusters. The challenges involved in deploying and managing microservices led to the creation of the service mesh, but service mesh solutions themselves introduce complexities and challenges.

When enterprises transition to a microservices model, they often need to migrate their legacy applications to the new infrastructure. One popular framework used for these traditional applications is.Net. Due to migration, enterprises require the ability to run Windows containers in their Kubernetes infrastructure.

workloads from remote clusters As Kubernetes continues to gain traction in the cloud-native ecosystem, the need for robust, scalable, and highly available cluster deployments has become more noticeable. While a Kubernetes cluster can easily expand via additional nodes, the downside of such an approach is that you might have to spend a lot of time troubleshooting the underlying networking or managing and updating resources between clusters.

In the rapidly evolving landscape of IT infrastructure, enterprises are increasingly moving away from traditional virtualization platforms due to rising licensing costs and the limitations these older systems impose on modern cloud-native application needs. The shift towards Kubernetes, which can manage diverse workloads such as containers, virtual machines (VMs), and bare metal environments, accelerates the migration from traditional virtualization platforms.

A common challenge with multi-cluster connectivity is that networking solutions have a prerequisite of pod-to-pod connectivity enabled between the clusters that are to be meshed. This typically requires the user to take care of the underlying network infrastructure in terms of routing.

Kubernetes is used everywhere, from test environments to the most critical production foundations that we use daily, making it undoubtedly a de facto in cloud computing. While this is great news for everyone who works with, administers, and expands Kubernetes, the downside is that it makes Kubernetes a favorable target for malicious actors. Malicious actors typically exploit flaws in the system to gain access to a portion of the environment.

System observability is an essential part of identifying performance issues within your environment because it provides a comprehensive view of how your systems are operating at a glance. Typically, observability is achieved through the collection and analysis of metrics. These metrics, generated by your applications, are deliberately incorporated by developers into the source code to offer insights into the application’s internal processes.

For DevOps and platform teams working with containers and Kubernetes, reducing downtime and improving security posture is crucial. A clear understanding of network topology, service interactions, and workload dependencies is required in cloud-native applications. This is essential for securing and optimizing the Kubernetes deployment and minimizing response time in the event of failure.