Serverless applications require a whole new approach to development workflow. In this article, Lumigo Director of Engineering Efi Merdler-Kravitz details the guiding principles and tools used at a 100% serverless company to ensure the most efficient workflow possible. We are not going to talk about product development flow (no product managers were harmed during the making of this post!).
As a company providing tooling to enable developers and operations teams to adopt a productive serverless workflow, Stackery is closely integrated with Amazon Web Services (AWS). Our customers are development teams who want to confidently build on AWS serverless and then manage the pipeline to production with confidence that what gets built and changed is done efficiently and well.
For adoption of cloud, for enterprises, there are tools which enable movement of VMs or hosts to the cloud. However, to seek the benefits of cloud, enterprises or startups need to focus on applications and not the entire infra over which the application resides. To ensure applications work post-migration, they need to be assessed on multiple parameters.
I am delighted to announce a big new initiative that we have been working on here at Squared Up. Our engineers have been working their socks off to build a new product, Squared Up for Azure, and it’s shaping up very nicely. It’s not ready for you to play with quite yet, but we plan to have an early release available for enthusiastic testers in late September (sign up below!).
On AWS, your workloads will be as secure as you make them. The Shared Responsibility Model in which AWS operates ensures the security of the cloud, but what’s in the cloud needs to be secured by the user. This means that as a DevSecOps professional, you need to be proactive about securing your workloads in the Amazon cloud. Achieving the optimal level of security in a multi-cloud environment requires centralized, automated solutions.
A big part of Checkly runs on AWS Lambda, but I never really discussed it in depth before on this blog. So here we go. Topics are: Note, I'm using "Lambda" here as a stand in for "serverless" in general. Many of the things discussed here apply to either Google Cloud Functions, Azure Functions and possibly Zeit although I've never used it. First something on how we use Lambda. Last week we went over 35 million check runs.
Cloudsmith has been officially approved for G-Cloud 11. We help organisations secure and accelerate their pipelines with a universal storage solution for packages and containers. Now on G-Cloud Digital Marketplace.
AWS Lambda is a service that confuses many people. For that reason, you may be wondering just how it works, and how you’d use it to build a highly scalable event-driven application. As someone who’s presumably no stranger to the internet, you must have seen the terms serverless, function-as-a-service, or AWS Lambda thrown across your screen a few times. Perhaps you’re looking to learn more. If so, you’re in luck.
This post takes a look at Kotlin, a relatively new programming language that runs on the JVM, and explores how it can be used to build serverless web apps on AWS using Lambda and API Gateway. In programming language terms Kotlin is a relative newcomer. It was first announced in 2011 by JetBrains, the makers of IntelliJ IDEA, and was designed as a modern successor to Java.
With AWS Lambda, you have basic observability built into the platform with CloudWatch. CloudWatch offers support for both metrics and logging. CloudWatch Metrics gives you basic metrics, visualization and alerting while CloudWatch Logs captures everything that is written to stdout and stderr. In this post, we will take a deep dive into CloudWatch Metrics to see how you can use it to monitor your Lambda functions and its limitations.
