Summary
In this chapter, we gained hands-on experience deploying Kubernetes clusters and running applications in them. We started by installing kubectl and deploying a local Kubernetes cluster using Kind. Then, we deployed managed Kubernetes clusters on AWS, GCP, and Azure. While the cloud providers differ, Kubernetes provides a consistent environment to run containers.
After setting up our clusters, we containerized and deployed the simple API application from Chapter 1. This demonstrated how to define Kubernetes deployments, services, ingress, and load balancers to run web applications. Then, we deployed the data processing batch job from Chapter 1 as a Kubernetes job. This showed us how to leverage Kubernetes for running one-off tasks and jobs.
By going through the process of deploying clusters and applications end-to-end, you now have first-hand experience with Kubernetes. You understand how to package applications as containers, expose them via services, ingress, or load...
