Developers love containers for their portability and flexibility. Suitable for today’s cloud-native environment and agile development requirements, containers make it super-fast to develop, test, and run applications. Besides this, they are lightweight and can optimize the platform (and the host OS) they are deployed on. Now, for powerful applications with hundreds of containers, the platform should also be portable, flexible, extensible, and efficient.

We are pleased to announce today that Shipa, the cloud-native Application as Code platform is now available on the AWS Marketplace. Bringing the power of Shipa to your internal customers on AWS infrastructure has never been easier with click to install from the AWS Marketplace.

Today, we have some exciting news to share: Pepperdata Capacity Optimizer now supports Azure Kubernetes Service (AKS). This means users of AKS are able to automatically optimize their workloads running on the Azure Kubernetes Service. AKS users can now rely on Pepperdata to help them monitor and optimize Spark applications on Kubernetes AKS. This evolution of our platform has been in the works for some time, and we know our users will be delighted by this development.

Kubernetes is one of the most prominent container orchestration platforms available today. As cloud-native- and container solutions gain attention, so is Kubernetes. The platform that Google open-sourced in 2014 has even become the standard for container management for private- and public cloud. With the new approach towards cloud-native application development, where microservices and containers are essential, there is a big focus on software development and how to migrate to the cloud.

Kubernetes has experienced rapid growth over the years, with a recent post from the Cloud Native Computing Foundation reporting a userbase increase of about 67% in just the past year. Kubernetes is a container orchestration platform that automates how containers are deployed, how they communicate, and how traffic is routed between them; it also scales configurations for both the containerized workloads and the underlying infrastructure that comprises the cluster.

At VMware, as we talk to enterprise customers about their application deployment patterns, challenges, and future requirements, we observe a common theme. Most of them are embarking on a modern application design and deployment path by using containers and Kubernetes as foundational technologies and by implementing their applications as microservices.

The global chip shortage, which began in 2020, continues as demand for semiconductor chips continues to far outpace production. Intel CEO Pat Gelsinger recently forecast shortages to be sustained through at least the remainder of 2022. As a result, IT operations teams at almost every company we’ve talked with have felt the crunch in the form of skyrocketing prices and delays of up to a year for procurement of physical servers.

Big news! The Qovery Terraform Provider is now publicly available for everyone! This article will show you what you can do and why you should consider using Terraform with Qovery.

In today’s world, graphics processing units or GPUs have attracted a lot of attention as the optimal vehicle to run artificial intelligence (AI), machine learning (ML) and deep learning (DL) workloads. These workloads require massive amounts of data, both ultra-high speed and parallel processing, along with flexibility and high availability. It is clear that high-performance computing (HPC) with graphics processing unit (GPU) systems are required to support cutting-edge workloads.

When we talk about various deployment and infrastructure provisioning choices on AWS, each option serves a particular set of users and needs. Some of Amazon's most common deployment services include Elastic Beanstalk, CloudFormation, and CodeDeploy. In containerization, there are options like ECS, EKS, Fargate, etc.