Serverless or Function-as-a-Service (FaaS) design patterns have been picking up steam. With the recent release of KNative from Google Cloud, let’s take a closer look at the serverless movement.
We live in exciting and worrying times. In serverless and containers, we have two amazing technologies that provide productive, machine-agnostic abstractions for engineers to work with. And yet, there seems to be an unbridgeable chasm between the two camps. If you have read anything I wrote in the last two years, you know that I am firmly in the serverless camp. But I was also an early adopter of containers.
In this article, Serverless computing (Functions as a Service), specificallyAWS Lambda for certain jobs is explored to explain the basics and reasoning for why it makes sense to use– with some example use-cases around OpsGenie. But first, let’s talk about the current cloud services available for running software.
Last week I attended ServerlessConf SF. This was the 8th and largest ServerlessConf to date with more than 550 attendees. The conference had an effective agenda with a strong slate of talks and an impressive presence of community thought leaders. Kudos to the ACloudGuru team for organizing the event!
Serverless compute services such as AWS Lambda offer many advantages to their customers, most notably the ability to reduce costs and minimise infrastructure. But serverless can also bring trade-offs that give rise to application performance issues.
Among other terms under the umbrella concept of cloud computing, we can find the Serverless technology which we have witnessed its growing popularity in the last three years. In principle, the cloud computing concept covers three levels of service: IaaS, Paas and SaaS.
Antoni Orfin, Chief Architect at Droplr, has years of hands-on experience building scalable web applications that serve traffic for millions of users across the globe. His journey started with bare-metal infrastructures. Then, he dove deep into cloud. Today, he’s responsible for making things run smoothly on state-of-the-art serverless architecture at Droplr.
Today’s post is a detailed guide on how to send custom metrics to AWS CloudWatch monitoring using AWS Lambda. Starting from scratch, we’re going to finish up this post by leaving a serverless project running on Amazon Web Services (AWS). And do you want to hear the best part? Getting started will cost you less than you might spend on a cheeseburger.
Serverless apps are growing in popularity, thanks to tools like AWS API Gateway and Lambda, and a growing number of powerful frameworks that simplify development and deployment. Complex applications are still complex, however, and regardless of your platform you’ll still need to think about observability.
Just a quick post highlighting some of the changes made since Apex Up v0.5.0! If you’re unfamiliar with Up, it’s a command-line tool to help you deploy and manage near-infinitely scalable serverless web applications and APIs on AWS. You can deploy as many applications as you need, for the fraction of the price of other providers, with zero operational maintenance or manual scaling efforts.
