aws-cloudformation-coverage-roadmap VS kubernetes

Given AWS CloudFormation is AWS's native language and service for infrastructure as code, you will likely find more official quickstarts provided by AWS in the language. In addition to this, AWS Support will probably be more capable of assisting you with issues when you need help. AWS Support is essential for large enterprises, particularly those new to the cloud or slow to adopt. These types of organizations may have a skill gap within their organization regarding their cloud skill set, and in turn, they are more likely to use AWS Enterprise Support.

Today, I will show you how to build Amazon Location Service, which allows you to build location-based applications within your AWS environment using AWS Cloud Development Kit (AWS CDK) and AWS CloudFormation. I will also show examples of the recently popular CDK Migrate and AWS CloudFormation IaC generator.

AWS CloudFormation: Speed up cloud provisioning with infrastructure as code.

Infrastructure as Code (IaC) is an important part of any true hosting operation in the public cloud. Each of these platforms has their own IaC solution, e.g. AWS CloudFormation. But they also support popular open-source IaC tools like Pulumi or Terraform. A category of tools that also needs to be discussed is API gateways and other app-specific load balancers. There are applications for internal consumption, which can be called microservices if you have a lot of them. And often microservices use advanced networking options such as a service mesh instead of just the native private network offered by a VPC.

Cloudformation (IaC) does not need to be introduced to anyone, plus if you read the previous blogpost, the terraform provider (CC) we used is based on Cloudformation. Moreover, you will notice a lot of similarities, after all, we are implementing the same scenario, but with a different tool.

AWS Application Composer launched in the AWS Console at re:Invent one year ago, and this re:Invent it expanded to the VS Code IDE as part of the AWS Toolkit - but that’s not the only exciting part. When using App Composer in the IDE, users also get access to a generative AI partner that will help them write infrastructure as code (IaC) for all 1100+ AWS CloudFormation resources that Application Composer now supports.

CloudFormation

Of course, best practices today dictate that we should be deploying our infrastructure as code, using tools such as CloudFormation or Terraform.

If you're provisioning the above resources for the first time, you'll have to either configure Terraform to use specific AWS keys as you won't have OIDC connection yet. In my case, I chose to have those pre-requesites resources in a CloudFormation template and deploy them with StackSets.

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.