Serverless platforms like AWS Lambda have helped accelerate application development by removing the need to provision and manage infrastructure resources. However, serverless architecture presents new monitoring challenges. Because AWS Lambda handles underlying infrastructure for you, you don’t have access to system-level metrics. Instead, you have to monitor your Lambda functions for insight into their performance and resource usage.

One problem that pops up quite frequently when people try to build serverless applications with AWS API Gateway and AWS Lambda is Execution failed due to configuration error: Malformed Lambda proxy response. There is nothing worse than generic error messages that don’t tell you anything you need to fix the problem, right? And AWS isn’t particularly known for its error message design, if you can even call it that, let alone for giving you the means of fixing the problem.

With more and more businesses moving online, and homegrown entrepreneurs spinning up new online apps, they’re increasingly looking for an online development platform to help them easily build and deploy their sites.

For quick, scalable, highly-available web services, few options compare to AWS Lambda. Just provide your code, add a little configuration, and you're done! In this article, Milap Neupane will introduce us to Lambda, show us how to get it working with Ruby and the Serverless Framework, and discuss reasons to use — or to not use! — Lambda in production.

In this article, we’ll be discussing everything you need to know about the basics of AWS Lambda error handling and some popular methods using StepFunctions and X-Ray. Regardless if you’re an AWS Lambda expert or if you’re a new Lambda user, there’s always something new to learn.

Among all the new features and services that AWS announced during the re:Invent 2020, my favorites were definitely the AWS Lambda updates. And there were many! For example, your code execution is no longer rounded up to the nearest 100ms of duration for billing — you are now billed on a per millisecond. On top of that, AWS increased the Lambda’s memory capacity to 10 GB, and correspondingly the CPU capacity up to 6 vCPUs.

There is no doubt that organizations have a difficult task to address the current requirements for building and managing APIs, especially if they don’t use tools like API Management. APIs require up-to-date and strong documentation, increased security levels, comprehensive testing, routine versioning, and high-reliability to be appropriately utilized.

We all know that debugging serverless is time-consuming and hard and that AWS Console doesn’t make it much easier. CloudWatch isn’t quite known for its ease of use. Why? Well to start with, it has suboptimal search features, logs scattered across multiple buckets and groups, little visualization capability, and no structure of Lambda function invocations.

There is no doubt that organizations have a difficult task to address the current requirements for building and managing APIs, especially if they don’t use tools like API Management. APIs require up-to-date and strong documentation, increased security levels, comprehensive testing, routine versioning, and high-reliability to be appropriately utilized.