kubeshark VS kubernetes

The one similar product I had come across is Kubeshark (https://github.com/kubeshark/kubeshark). But admittedly the eBPF way seems more performant theoretically (given you can afford to have a modern-enough kernel). I'm really excited to see how this project develops out.

The eBPF-mode of innovation is pretty exciting, truly a fresh lens to building software. I'm also following Akita Software - the company building an eBPF paradigm of monitoring.

GitHub: https://github.com/kubeshark/kubeshark Website: https://kubeshark.co/

Can you send us the logs using: `kubeshark logs` (you can also use the issues page or slack)

Yes there are, but Cloud NAT alone won't be able to help you with that. You can either use a standalone proxy or a 3rd party firewall appliance through which you can route your traffic, or use more modern approaches with tools such as Kubeshark or Cillium.

https://github.com/kubeshark/kubeshark - somewhat like Wireshark for Kubernetes. LMK if you have any questions

For example, this open-source project gives instant visibility into API traffic in kubernetes environments: https://github.com/kubeshark/kubeshark

Check out the GitHub repository: https://github.com/kubeshark/kubeshark

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.

"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.