Kubernetes and Docker each play a vital role in modern, microservices-based application development. Since Kubernetes and Docker work in unison to help develop, deploy, and manage large-scale applications – they are not mutually exclusive technologies and they are certainly not in competition with each other. Nevertheless, Kubernetes and Docker are often misunderstood by the non-developer community. To clear up the confusion around Kubernetes vs. Docker, we’ve written this guide.

The “as a service” business model continues to grow rapidly, largely thanks to the rise of cloud computing. “As a service” offerings deliver IT products and technologies such as software, hardware, and data storage to consumers via the Internet, rather than having to install or manage them themselves. Serverless and containers are two such “as a service” technologies that have seen increasing adoption in recent years.

Kubernetes is an open-source platform for container orchestration. You can use it to deploy a highly resilient, self-healing infrastructure using automation and infrastructure as code (IaC). Kubernetes includes features for zero downtime deployments, scaling, automatic rollout and rollback of updates, and service discovery. Kubernetes is designed to help you manage container deployments at scale via REST API.

If your company has already started getting into machine learning / deep learning, you will quickly relate to the following story. If your company is taking its first steps into data-science, here is what is about to be dropped on you. If none of the above strikes a chord, well it’s probably good to know what’s out there because data-science is all the rage now, and it won’t be long until it gets you too 🙂

As new advances in software development have allowed developers to increase their velocity and push out new software at ever increasing speeds, one less measured metric is software understandability. Although it probably seems obvious, when building new software the goal should always be to build software that is as simple and easy to understand as possible.

Kubernetes (k8s) enables you to efficiently orchestrate container management, in the cloud or on-premises. As a whole, k8s provides many benefits, including features for self-healing, automated rollouts and rollbacks, load distribution, and scalability. However, k8s is a highly complex platform and requires extensive configuration.

Once upon a time, we had these giant structures where thousands of people would congregate to share ideas, pamphlets filled to the margins with buzz words and cheap, branded t-shirts. Yep, tech conferences – oh what a relic of the past that I miss. It used to be part of my job to attend these.

This article demonstrates how to Load Test an application deployed in a Kubernetes cluster, verify that the autoscaling is working, and identify potential performance bottlenecks. When you deploy an application to production on Kubernetes, you'll need to monitor traffic and scale up the application to meet heavy workloads during various times of the day. You'll also need to scale down your application to avoid incurring unnecessary costs.

Kong for Kubernetes is a Kubernetes Ingress Controller and a full-fledged edge-router which can route traffic to any destination of your choice. In addition to Ingress management, it provides enhanced security and management capabilities. With Kong, you can use Kubernetes not just for running your workloads but also for securing and monitoring connectivity between your workloads – all managed via Kubernetes manifests and CI/CD processes.