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.

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?

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.

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.

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.

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.

This post was co-written by Kirti Apte and Gabry (Maria Gabriella) Brodi. Over the last couple of years, customers have been adopting Kubernetes and microservice-based application deployment models for various technology and business reasons. In fact, there is a trend that customers are now looking to the next set of use cases that include applications across multiple clouds, as well as edge clouds.

Our latest open-source project, Helm-Dashboard, just crossed 3K stars on GitHub (and hundreds of daily active users), only three months since it was released! We thought this milestone was a good chance to take a look back at our journey, announce the release of v. 1.0.0, discuss future plans, and, most importantly, give our utmost thanks to the amazing contributors and Kommunity members that made it all possible! What capabilities would you like to see next in Helm-Dashbaord?

The first thing to know when exploring APIs vs microservices is that there’s not a dichotomous relationship between these two technical approaches. Instead, you want to build an understanding of how these two technologies relate to each other—and they do, in a big way. So, organizations don’t need to embrace either an API-first or microservices-first strategy.

VMware Tanzu Kubernetes Grid has become a trusted tool to automate the lifecycle of Kubernetes clusters. The simplicity of management has quickly turned it into a valuable asset in many organizations. The release of Tanzu Kubernetes Grid 2.1 adds new features that enhance lifecycle management and extends Kubernetes to the edge.