There is a paradox at the heart of serverless. While it’s promoted as a very agile way to develop, a way to push your product as fast as possible to your customers, many development teams find it really difficult to work fast. Why does this happen and can we solve the issue? Let’s start with what makes serverless such a powerful paradigm. Serverless rests on three legs, each of which contributes to the agility that serverless is known for.
Serverless computing is becoming more popular as organizations look for new ways to deploy their applications in the cloud. With higher levels of abstraction, easier maintenance, a focus on high performance, and ephemeral workloads, serverless computing solutions like Lambda are finding a permanent place in the mix of cloud infrastructure options.
Serverless has become something of a buzzword lately - but it’s much more than a trend. Its benefits have gained the attention of professionals and amateurs in the tech industry, mainly due to how much cloud vendors have raved about the design. For mid-sized ventures, the idea of serverless is exceptionally appealing, for those looking for budget-friendly, but useful pieces of technology. It’s genuinely breaking the ground and is sure to make even more moves in the tech industry.
AWS Lambda has gained good traction for building applications on AWS. But, is it really the best fit for all use cases? Since its introduction in 2014, Lambda has seen enthusiastic adoption – by startups and enterprises alike. There is no doubt that it marks a significant evolution in cloud computing, leveraging the possibilities of the cloud to offer distinct advantages over a more traditional model such as EC2.
A common pattern in serverless architecture is to have a queue before a function. This is great because you can create a second queue for all of the messages that failed in the function execution (or, if we want to put it in terms that don’t sound like we’re aggressively shaming them, we can classify them as having “encountered an error at some point”). This second queue is known as a “dead letter queue” or DLQ for short.
A common pattern in serverless architecture is to have a queue before a function. This is great because you can create a second queue for all of the messages that failed in the function execution (or, if we want to put it in terms that don’t sound like we’re aggressively shaming them, we can classify them as having “encountered an error at some point”). This second queue is known as a “dead letter queue” or DLQ for short.
Many clients have asked me, “When do I use DynamoDB streams vs Kinesis?” It’s a great question, since both services are very similar, especially when you process their records with Lambda functions. So let’s break it down and look at the key differences between Kinesis and DynamoDB streams. When it comes to streaming and processing real-time events on AWS, Kinesis is the de facto solution in AWS.
Last week I was battling jet lag and this week, an inbox of unanswered emails. But I’m also marveling at the quality of the AWS community all over the world. I had the privilege of speaking at ServerlessDays Sydney the week before last, and along with the amazing conference talks, I also got to see firsthand just how much of the globe is involved in pushing forward serverless using AWS technology and how excited they were to share what they were learning.
Since it was released in 2015, the Serverless Framework has become the community-favorite way to manage and deploy serverless applications in the cloud. Similar to Terraform and CloudFormation, it lets you express infrastructure as code, making it easy to share and version-control your entire serverless environment.
Seed is a fully managed CI/CD pipeline for Serverless Framework applications. In one of our previous posts we looked at how to build a CI/CD pipeline for Serverless apps on AWS with CircleCI. Today, we’ll look at how to do the same with Travis CI. The purpose of these posts is to dive deep into real-world CI/CD setups, something which most of the tutorials out there skip. We’ll try to illustrate how to set up something similar to Seed but using Travis CI instead.
