aws-node-termination-handler VS kubernetes

Disaster recovery is a critical component of any IT infrastructure. It ensures that your applications and data are protected in the event of an unexpected outage or disaster. In this blog post, we will explore different disaster recovery strategies for Amazon Elastic Compute Cloud (EC2) deployments.

When you are using compute you have a lot of options. One of these options is Amazon EC2. In a world where more and more workloads become serverless. You might still have this use-case that is better off on EC2. But, how do you combine EC2 with compliance and security? In this blog post we will explore how we can build a compliant and secure EC2 stack.

In this article, a WEB application using the latest version of Angular in a built Docker image will be hosted on Amazon EC2 (Elastic Compute Cloud) and deployed by Amazon ECS (Elastic Container Service) using an Amazon ECR (Elastic Container Registry) containers repository.

The single most important development in hosting since the invention of EC2 is defined by its own 3-letter acronym: k8s. Kubernetes has won the “container orchestrator” space, becoming the default way that teams across industries are managing their compute nodes and scheduling their workloads, from data pipelines to web services.

EC2

Then in 2020, I started working with AWS. My first two years with AWS were mostly interacting with the Node.js apps I've deployed in EC2 and reviewing logs since we had a DevOps engineer who managed the cloud infrastructure.

If you want to learn more about EC2 and get detailed information, I suggest you start your journey by visiting https://aws.amazon.com/ec2/. This is the best place to begin learning about EC2.

AWS - Cloud Computing AWS - EC2 Wikipedia - Cloud Computing Guru99 - Cloud Computing Cloudflare - Cloud Computing Cloudzero - Statistics Zippia - Statistics

Say that we have an application running behind a public-facing Application Load Balancer (ALB). The load balancer's target can be any supported target, including ECS containers, EC2 instances or even Lambda functions. Because the application is only available to authenticated users, we want to find a solution to identify them.

Although I'll use Lambda functions in the examples, we can transfer the concepts to other compute resources, like EC2 instances, and ECS or EKS containers.

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.