Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes is a powerful tool for deploying and managing containerized applications. However, managing its clusters is a critical but challenging task for many organizations. Today, we will discuss the benefits of managing multiple clusters, and the challenges they present, and offer a quick technical guide on managing these clusters efficiently.

In this guide, we document the process of building mock APIs in Kubernetes from traffic using Speedscale.

The emergence of Ephemeral Environments poses a compelling question: What can organizations expect from adopting this transformative approach? What are the hurdles they may face along the way? And what results can they anticipate? These are the questions I will address in this article as we explore why Ephemeral Environments are garnering attention and what makes them so pivotal for engineering teams today. Let's go!

There’s more pressure than ever to deliver high-availability Kubernetes systems, but there’s a combination of organizational and technological hurdles that make this ‌easier said than done. Technologically, Kubernetes is complex and ephemeral, with deployments that span infrastructure, cluster, node, and pod layers. And like with any complex and ephemeral system, the large amount of constantly-changing parts opens the possibility for sudden, unexpected failures.

In the dynamic world of container orchestration, Kubernetes stands out as the undisputed champion, empowering organizations to scale and deploy applications seamlessly. Yet, as the deployment scope increases, so do the associated Kubernetes workload costs, and the need for effective resource capacity planning becomes more critical than ever. When dealing with containers and Kubernetes you can find yourself facing multiple challenges that can affect your cluster stability and your business performance.

In the software development landscape, mastering test automation has become crucial for ensuring efficient quality assurance (QA) processes. Test automation not only accelerates testing cycles but also enhances test coverage and accuracy, leading to higher-quality software releases. This blog explores key strategies for mastering test automation and achieving efficient QA outcomes.

Selector offers comprehensive monitoring, observability, and AIOps solutions for service providers and enterprises. The process begins with collecting, aggregating, and analyzing multi-domain operational data from various sources, such as SNMP, streaming telemetry, syslogs, and Kafka. Selector then applies advanced AI/ML techniques to power features such as anomaly detection, event correlation, root cause analysis (RCA), smart alerting, and a conversational GenAI-driven chat tool, Selector Copilot.