kubernetes VS aws-lambda-java-libs

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.

FaaS is specifically focused on building and running applications as a set of independent functions or microservices. Major cloud providers like AWS (Lambda), Microsoft Azure (Functions), and Google Cloud (Cloud Functions) offer FaaS platforms that allow developers to write and deploy individual functions without managing the underlying infrastructure.

So AWS Lambda is basically a serverless computing service that is offered by AWS. It enables developers to run the code in response to various events. It protects the developers from the pain of managing the servers. Using a serverless execution model helps the developers to handle provision, manage and scale the servers automatically. Through this approach the developers can fully focus on writing the code instead of dealing with other aspects.

The first product that popularized the term “serverless” was AWS Lambda, which is both the prototypical and archetypical function as a service provider. It also has a great name, which pings back to its envisioned place in the cloud of the future. In computer programming, a lambda, often referred to as a lambda function or lambda expression, is a concise way to represent an anonymous function, which is a function without a name. The concept originates from lambda calculus in mathematical logic and has been adopted by many programming languages, each with its own syntax and characteristics.

You can build a custom config rules in 2 ways, using AWS Lambda and CloudFormation Guard. Lambda gives you a lot of flexibility, but it also brings complexity of maintaining. CloudFormation Guard is a bit more lightweight in that regard. Yes, you still need to maintain the logic to determine when your resource is compliant or not. But you need to do this in both cases, thus my go to preference is CloudFormation Guard.

AWS Lambda: https://aws.amazon.com/lambda/

This blog details how you can use some key serverless components from AWS like Amazon Eventbridge, AWS Lambda, and Simple Notification Service to setup a system that will monitor your site (which can be running anywhere) and send emails, text messages, slack messages, and more when the reachability status of your site changes.

I use the OpenWeather API to receive the current temperature data by providing the latitude and longitude coordinates as query strings in the request. A Lambda function invokes the weather API and sends the current temperature value as a custom metric to CloudWatch. I then graph the temperature values on a dashboard and activate an alarm when the temperature sinks below zero degrees Celsius.

After two years, I moved to a Web3 startup where I was given a lead software engineer role. This new role gave me more hands-on experience with AWS, where I've learned to implement serverless technologies like Lambda and DynamoDB.

I discussed a way to control access to endpoints using JSON web tokens and a Lambda authorizer earlier.