gatekeeper-library VS kubernetes

Here is a library or rules for the Open Policy Agent.

https://github.com/open-policy-agent/gatekeeper-library is the library of OPA Gatekeeper policies.

Many more examples are available in the OPA Gatekeeper library project!

by default and exposes metrics on path ```/metrics``` . It can run locally on your development box as long as you have a valid Kubernetes configuration in your home folder (i.e. if you can run kubectl and have the right permissions). When running on the cluster a ```incluster``` parameter is passed in so that it knows where to look up for the cluster credentials. Exporter program connects to Kubernetes API every 10 seconds to scrape data from Kubernetes API. We've used [this](https://medium.com/teamzerolabs/15-steps-to-write-an-application-prometheus-exporter-in-go-9746b4520e26) blog post as the base for the code. ## Demo Let's go ahead and prepare our components so that we have a Grafana dashboard to show us which constraints have been violated and how the number of violations evolve over time. ### 0) Required tools - [Git](https://git-scm.com/downloads): A git cli is required to checkout the repo and - [Kubectl](https://kubernetes.io/docs/tasks/tools/) and a working K8S cluster - [Ytt](https://carvel.dev/ytt/): This is a very powerful yaml templating tool, in our setup it's used for dynamically overlaying a key/value pair in all constraints. It's similar to Kustomize, it's more flexibel than Kustomize and heavily used in some [Tanzu](https://tanzu.vmware.com/tanzu) products. - [Kustomize](https://kustomize.io/): Gatekeeper-library relies on Kustomize, so we need it too. - [Helm](https://helm.sh/): We will install Prometheus and Grafana using helm - Optional: [Docker](https://www.docker.com/products/docker-desktop): Docker is only optional as we already publish the required image on dockerhub. ### 1) Git submodule update Run ```git submodule update --init``` to download gatekeeper-library dependency. This command will download the [gatekeeper-library](https://github.com/open-policy-agent/gatekeeper-library) dependency into folder ```gatekeeper-library/library``` . ### 2) Install OPA/Gatekeeper If your K8S cluster does not come with Gatekeeper preinstalled, you can use install it as explained [here](https://open-policy-agent.github.io/gatekeeper/website/docs/install/). If you are familiar with helm, the easiest way to install is as follows: ```bash helm repo add gatekeeper https://open-policy-agent.github.io/gatekeeper/charts helm install gatekeeper/gatekeeper --generate-name

Gatekeeper library has example policies for restricting image repositories: https://github.com/open-policy-agent/gatekeeper-library/tree/master/library/general

You should check out the Gatekeeper project. There's plenty of templates available for use without having to write a single line of rego for most use cases (e.g https://github.com/open-policy-agent/gatekeeper-library)

I’m joking of course. I’m not really sure about what a faded keyboard says about its owner. What I do know for sure is how important kubectl is to anybody who wants to be a proficient Kubernetes administrator.

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

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.