external-dns VS kubernetes

The second one is a combination of tools: External DNS, cert-manager, and NGINX ingress. Using these as a stack, you can quickly deploy an application, making it available through a DNS with a TLS without much effort via simple annotations. When I first discovered External DNS, I was amazed at its quality.

One of the reasons why I chose Hetzner was that it WAS supported by the ExternalDNS project. I didn't quite understand why the Hetzner provider was pulled, but I saw that an attempt of re-adding it was refused, on the ground that the upcoming webhook architecture would have allowed to better maintain providers.

Write a custom controller for the external DNS controller, or setup some form of ArgoCD app / appset templating.

so I am looking at having an automated way for new routers registered in Traefik to also have the corresponding DNS entry added to my Pihole instance similar to external-dns but obviously, this is exclusive to ingress on k8s environments. my current setup is traefik in a container on unraid.

You technically don’t need to run a load balancer or have a virtual IP for your control plane. If you control your dns, you can add an A record pointing to all IPs for your control plane nodes. It won’t load balance your traffic, but combined with something like External DNS it gives you HA for the control plane.

I normally deploy external-dns, which automatically updates DNS with the ingress controller's external IP address.

Background: Having a look I can see this https://github.com/kubernetes-sigs/external-dns

3) Use the Kubernetes External-DNS. I've never used this, but this is assuming it can update DNS for each pods/app to point to the correct Node (it'd need to update my Homelab DNS running on Windows Server)

So here’s the solution I have taken for our current stack. EKS and its dependencies are created through terraform using the eks module as well as provision a route53 subdomain and a wildcard cert. Once we have that created, I have installed this deployment into the cluster via the helm module: https://kubernetes-sigs.github.io/aws-load-balancer-controller/v2.4/. This allows me to use kuberentes resources (load balancers or ingress objects) and it will handle all the provisioning of load balancers and security groups for me, based on my application yaml and annotations. We also use https://github.com/kubernetes-sigs/external-dns to manage all of our specific host names for the applications through annotations. So to generally put, terraform manages out Kubernetes clusters, and Kubernetes manages the deployment of anything needed for the application including volumes, load balancers, hostnames though Kubernetes system deployments

Metallb for your load balancer (layer2 mode will do) NginX-ingress, will be spot on for internal home apps External-dns to publish your dns record to your Dns server at home, https://github.com/kubernetes-sigs/external-dns

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.