Containers are a useful tool for deploying applications because they allow for the packaging of an application’s code, libraries, and dependencies into a single, isolated unit. This makes it easy to deploy and run the application on any computer or server that has the necessary container software installed.

Have you ever fallen asleep to the sounds of your on-call team in a Zoom call? If you’ve had the misfortune to sympathize with this experience, you likely understand the problem of Alert Fatigue firsthand. During an active incident, it can be exhausting to tease the upstream root cause from downstream noise while you’re context switching between your terminal and your alerts. This is where Alertmanager comes in, providing a way to mitigate each of the problems related to Alert Fatigue.

I am thrilled to announce that SUSE has been recognized by G2, the world’s largest and most trusted software marketplace, as one of the Top 25 German Companies in their “Best Software Awards” for 2023. At SUSE, we have always been dedicated to providing our customers with the best possible software solutions and services. This award by G2 is a testament to the hard work and dedication of our entire team.

Kubernetes is a powerful platform that comes with many features to help engineers run their applications more efficiently. However, as you gain more experience and deploy more workloads, you’ll inevitably start looking for ways to optimize Kubernetes performance. There are many ways to approach optimization. On one hand, you could work exclusively with the tools and configurations provided by Kubernetes itself; on the other, you could reap the benefits of third-party tools.

Kubernetes has come a long way, but the current state of Kubernetes open source monitoring is in need of improvement. This is in part due to the issues related to an unnecessary volume of data related to that monitoring. For example, a 3-node Kubernetes cluster with Prometheus will ship around 40,000 active series by default. Do we really need all that data?

Companies face multiple challenges when migrating their applications and services to the cloud, and one of them is infrastructure management. The ideal scenario would be that all workloads could be containerized. In that case, the organization could use a Kubernetes-based service, like Amazon Web Services (AWS), Google Cloud or Azure, to deploy and manage applications, services and storage in a cloud native environment. Unfortunately, this scenario isn’t always possible.

Hey there, Qovery users! We hope you're doing well. We've got some big news to share with you today about the cloud service providers available on our platform.

As the movement to the cloud has grown, so has the use of containers as an effective way to package, distribute, and deploy applications. As surveys show, Kubernetes is the most widely used orchestration engine for managing cloud-native containers. Kubernetes automates deployment, auto-scaling, resource optimization, backup and recovery, and enables containers to run across different environments, eliminating the need to develop separate versions for each operating environment.

This blog will cover what the Calico/VPP dataplane is and demonstrate the performance and flexibility advantages of using the VPP dataplane through a benchmarking setup. By the end of this blog post, you will have a clear understanding of how Calico/VPP dataplane, with the help of DPDK and accelerated memif interfaces, can provide high throughput and low-latency Kubernetes cluster networking for your environment.