In this article, we’ll be rewinding back to the very beginning of the AWS Well-Architected Framework to understand how and why it came to be, and why is it of utmost importance, but very often underrated, for serverless developers to learn, understand and apply this framework of best-practices. We’ll also be looking into how the framework has evolved and how it should be used in 2021.

AWS Lambda has become a core technology in the shift to cloud-native application development, eliminating infrastructure management and fixed costs. But there are trade-offs with serverless environments. Not having access to the production infrastructure can make debugging difficult and there are a lot of moving parts, adding distributed complexity. Monitoring serverless functions in production requires observability beyond CloudWatch logs and metrics.

This is an example machine learning image recognition stack using AWS Lambda Container Images. Lambda container images can include more source assets than traditional ZIP packages (10 GB vs 250 MB image sizes), allowing for larger ML models to be used. This example contains an AWS Lambda function that uses the Open Images Dataset TensorFlow model to detect objects in an image.

Here at Lumigo, our mission is to help customers succeed with serverless by solving the observability problem and letting them focus on adding business value with serverless technologies. And to do that, we love to eat our own dogfood – be it using the same serverless technologies that our customers use and even using Lumigo to monitor Lumigo itself! That way, we feel your pain and we find solutions to problems that you care about.

When building cloud-based systems and serverless systems, in particular, it’s crucial to stay on top of things. Your infrastructure will be miles away from you and not a device you hold in your hands like when you build a frontend. That’s why adding a monitoring solution to your stack, which offers a pre-configured serverless failure detection, should be one of the first decisions.

A platform-agnostic way of accessing credentials in Python. Even though AWS enables fine-grained access control via IAM roles, sometimes in our scripts we need to use credentials to external resources, not related to AWS, such as API keys, database credentials, or passwords of any kind. There are a myriad of ways of handling such sensitive data. In this article, I’ll show you an incredibly simple and effective way to manage that using AWS and Python.

Serverless provides benefits far beyond the ease of management…it strongly encourages “useful” engineering practices. Here’s how. It’s hard to determine what can be considered a “good” or “bad” engineering practice. We often hear about best practices, but everything really boils down to a specific use case. Therefore, I deliberately chose the word “useful” rather than “good” in the title.

In this article, we will explain how to monitor AWS SQS with Prometheus. To monitor AWS SQS, we will leverage the data offered by CloudWatch exporting the metrics to Prometheus using the YACE exporter (Yet Another CloudWatch Exporter). Finally, we will dive into what to monitor and what to alert. AWS SQS (Simple Queue Service) has gained popularity as a way to communicate and decouple asynchronous applications, specifically for its easy integration with AWS Lambda functions.

In this article, I’m trying to shine some light on the AWS Lambda Layers, Lambda Extensions, and Docker image for Lambda, in order to add third-party code to Lambda. When and how to use which method, and when to mix and match? Due to the circumstances in 2020, many software releases were postponed, and so the industry slowed its development speed quite a bit. But at least at AWS, some teams got updates out of the door at the end of the year. AWS Lambda got two significant improvements.