PODs/containers running on Kubernetes are just Docker images running some command.Deployed “applications”/PODs inside Kubernetes also will need a Service to communicate across nodes.(if you want to use Airflow UI you will need this.) Kubernetes will require something called a LoadBalancer to accept/ingress/route HTTP/Internet traffic from the outside world to inside the Kubernetes cluster and eventually to your “application.” If you require such a feature.You use YAML files to describe the system you are trying to/will deploy onto Kubernetes.You deploy PODs inside Kubernetes that will run/host different “pieces” of an application.They’re a few obvious and not so obvious things about deploying any application inside Kubernetes if you haven’t done it much before. we are going to need the following containers running inside Kubernetes. Basics of Deploying Airflow inside Kubernetes. But what you may not know is how you can actually deploy something like Airflow inside Kubernetes. I’m going to assume you know something about Kubernetes. Simply put, an Airflow installation usually consists of a Scheduler, Airflow Workers, Web Server/UI, and optionally a Database. I’m going to assume you know something about Apache Airflow, I’ve written about it before. Here is my $20/month cluster from Linode. I recommend using Linode, you can easily spin up a cheap cluster with the click of a button. The first thing you will need is a Kubernetes cluster. It’s actually so easy your mom could probably do it….maybe she did do it and just never told you? So why not deploy Airflow onto Kubernetes? This is what you wish your mom would have taught you. What more could you want? There is also no better tool than Kubernetes for building scalable, flexible data pipelines and hosting apps. I mean Python, a nice UI, dependency graphs/DAGs. Has anyone else noticed how popular Apache Airflow and Kubernetes have become lately? There is no better tool than Airflow for Data Engineers to built approachable and maintainable data pipelines.