As an orchestration engine, Apache Airflow let us quickly build pipelines in our data infrastructure. However, as our business grew to 2 billion orders delivered, scalability became an issue. Our solution came from a new Airflow version which let us pair it with Kubenetes, ensuring that our data infrastructure could keep up with our business growth.
We recently had the opportunity to interview VMware’s principal engineer, Joe Beda, one of the creators of Kubernetes, as well as the Google Compute Engine. Joe is an experienced software engineer who has worked at both Microsoft and Google. He also co-founded the cloud-native leader, Heptio, which is now a part of VMware. We have included the entire interview transcript below, and we hope you enjoy it!
Kubernetes is proven as the best fit in edge computing use cases that will further empower #CloudNative backhaul of the 5G network for the fastest service delivery. Kubernetes distributions like K3S, KubeEdge, and MicroK8S filled the exact gaps in orchestrating containers at the edge. Download our 2 part ebriefs to know everything about implementing Kubernetes for Edge.
This eBrief is created to guide the technical leaders in an organization about the key developments in the Kubernetes security landscape. This eBrief covers the following topics
What triggered the focus on K8S security?
Reports about Kubernetes security
How Kubernetes security is compromised?
How can you ensure security within the Kubernetes architecture?
This article explores the inspiration behind Ballerina and how it addresses the fallacies of distributed computing. Ballerina specializes in moving from code to cloud while providing a unique developer experience. Its compiler can be extended to read annotations defined in the source code and generate artifacts to deploy your code into different clouds. These artifacts can be Dockerfiles, Docker images, Kubernetes YAML files or serverless functions.
Kubernetes provides workload portability. That is, any workloads should spontaneously run on any type of infrastructure where Kubernetes clusters are deployed. In the case of handling stateful workloads, it may not easy to set up persistent storage but it is not impossible. The Kubernetes community has addressed the issue for stateful services and different storage options with CSI along with dynamic provisioning of persistent storage using storage classes. These allow integrating remote block/file storage easily into K8S clusters and can run on any different K8S-based clusters.
In this e-book, we will explore the above mentioned and alternative methodologies used by Kubernetes. We have reviewed Kubernetes for data storage and as an enabler for maximizing the efficiency of management and deployment of the containers, specifically in the light of stateful services.
You won't believe what K8s means! Check out the full article to find out. My intention for this post is to have at least two parts. The first part of Understanding Kubernetes will be theoretical and during the second we will make our hands dirty (practical).
Learn how to deploy a custom MEAN application from a GitHub repository to a Kubernetes cluster in three simple steps using Bitnami's Node.js Helm chart. After showing you how to deploy your application in a Kubernetes cluster, this article also explains how to modify the source code and publish a new version in Kubernetes using the Helm CLI.
This article talks about how Kubernetes has emerged from container orchestration platform to manage complex workloads in AI and Machine Learning Stacks, Managing containers in NFV architecture and handling hardware GPU resources.