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A Question and Answer session with guests: 

Micronaut is an open-source, JVM-based framework for building full-stack, modular, easily testable microservice and serverless applications. Unlike reflection-based IoC frameworks that load and cache reflection data for every single field, method, and constructor in your code, with Micronaut, your application startup time and memory consumption are not bound to the size of your codebase. Micronaut’s cloud support is built right in, including support for common discovery services, distributed tracing tools, and cloud runtimes. 

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There are three steps that Kubernetes uses to enforce security access and permissions — Authentication, Authorization and Admission. In this article we are going to consider Authentication first.

              The Authentication, Authorization and Admission Control Process

The first thing in Authentication is Identity.

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Just a couple of years ago, serverless was a sparsely used technology, recognized mostly for its cost-efficiency. Today, we’re seeing a radical shift in the attitude towards serverless. Developers have begun to realize that serverless not only minimizes operational overhead but can considerably enhance core applications in production. As cloud vendors continue to develop their serverless offerings, serverless is being used in production more than ever before.

In this blog post, we explain what serverless is, dive into some of its major advantages, and point out some factors contributing to its growing popularity.

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Camel K, a project under the famous Apache Camel project, is a project that totally changes the way developers work with Kubernetes/OpenShift cloud platforms by automating the nasty configuration and loads of prep work from developers. If you are an old-time developer like me, you did your best to slowly try to adapt to the latest and greatest cloud native “ecology.” It’s not difficult, but with small things and traps here and there. I’ll tell you it’s not a smooth ride. It’s understandable for emerging technologies. But with the large adoption of cloud, I see it’s reaching a level of maturity, where now we are thinking of how to make things go faster, as well as making it more accessible to the larger audience. 

Check out some reasons why you might love Camel K.

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Durable cloud event production with Knative

Knative is an excellent platform for building, deploying and managing serverless workloads on Kubernetes. The Serving resources of Knative extend Istio to support serverless applications. Another class of resources of Knative called Eventing extend Istio to support the production and consumption of Cloud Events.

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Here’s the context of how the need for AWS Eventbridge came to be.

As the march of technology is never-ending, the only constant we can expect is change. This is especially true considering the strides that serverless has made in the industry, especially with the release of the AWS Lambda back in 2014. Upon its release, AWS Lambda was quick to take front and center position in the FaaS services making up the core of serverless applications. It was rightly heralded as one of the most important releases within the domain. This further lead to an array of best practices dictating how applications were built using FaaS services achieving serverless capabilities. 

You may also enjoy:  Explaining Eventbridge Amidst the Hype

However, as the course of technology meanders in its ongoing path, new innovations are constantly redefining the way we build applications. One such innovative service announced this year was AWS EventBridge, and its release has since caused an uproar in the domain of serverless. Many blogs and posts within the community that followed the announcement characterized it as the most important announcement after the release of AWS Lambda. 

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Learn to set up and use Fission, an open-source serverless framework that lets you write functions and execute them on-demand to make sure you only use the resources you need.
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Handling large images has always been a pain in my side since I started writing code. Lately, it has started to have a huge impact on page speed and SEO ranking. If your website has poorly optimized images it won’t score well on Google Lighthouse. If it doesn’t score well, it won’t be on the first page of Google. That sucks.

TL;DR

I’ve built and open-sourced a snippet of code that automates the process of creating and deploying an image resize function and an S3 bucket with one simple command. Check out the code here.

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What is serverless? Serverless is a cloud computing model that was first introduced by AWS in 2014 (AWS Lambda is the market leader to this day). It abstracts away most of the server operations to the cloud provider so developers can only focus on writing code and shipping new features. Serverless adoption is rapidly growing and it’s mostly due to it’s promise of providing significant cost and time efficiency for technology companies.

For new startups, just starting to build their first product, it’s a no-brainer to build it on serverless. It’s very cheap and quick to get started. You don’t have to have that much knowledge about the backend processes, since the cloud providers handle all that and you can just focus on building your product and it’s functionality. The go-to-market time is much shorter than with previous computing models and it scales automatically when you get successful and have tons of users pouring on your site. See, a no-brainer!

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Azure Functions

As part of Microsoft’s offer in the Platform-as-a-Service model, Azure Functions is one of Azure’s Serverless alternatives. Launched in late 2016, currently, it has two versions of runtimes available, although the version 2.x is still in preview mode. This new version is adding Java as a programming language to those supported by version 1.x — C#, JavaScript, and F#.

Azure Functions is the on-demand execution of functions or small fragments of code based on events. As the cost is associated with the execution, if the function is not executed, there are no charges to pay. And if the event that triggers the execution occurs faster than the function, the platform executes the code in multiple threads without obstacles.

Source de l’article sur DZONE