I’d like to take a moment to point out some of my favorite things that have come across my Twitter timeline in the last week, if you’d like to see more follow Stackery on twitter!

You know what they say: successful deploys are all alike; every unsuccessful deploy is unsuccessful in its own way (ok, no one actually says that, except engineers who have read way too much Russian literature, ahem).

AWS Lambda decouples the need to provision and maintain a runtime environment from running code, allowing developers to focus on applications rather than infrastructure. But, by abstracting away the underlying infrastructure of an application, serverless architectures introduce new challenges into monitoring and observability.

AWS Lambda is a compute service that enables you to build serverless applications without the need to provision or maintain infrastructure resources (e.g., server capacity, network, security patches). AWS Lambda is event driven, meaning it triggers in response to events from other services, such as API calls from Amazon API Gateway or changes to a DynamoDB table.

In Part 1 of this series, we discussed AWS Lambda functions and some key metrics for monitoring them. In this post, we’ll look at using Amazon’s native tooling to query those metrics. We’ll also show you how to collect logs and traces that provide further visibility into your Lambda functions. Amazon provides built-in monitoring functionality through CloudWatch and X-Ray.

In Part 2 of this series, we looked at how Amazon’s built-in monitoring services can help you get insights into all of your AWS Lambda functions. In this post, we’ll show you how to use Datadog to monitor all of the metrics emitted by Lambda, as well as function logs and performance data, to get a complete picture of your serverless applications. In this post, we will: Datadog integrates with AWS Lambda and other services such as Amazon API Gateway, S3, and DynamoDB.

Since AWS Lambda was launched in 2014, serverless has transformed the way applications are built, deployed, and managed. By abstracting away the underlying infrastructure, developers are able to shift operational responsibilities to the cloud provider and focus on solving customer problems.

Recently, I had a scenario in which it was necessary to access the content of the source JSON message and extract a property that contains a date-time. That property was in the last object (a repeating object) for control purposes, and my first idea was to get it done with Azure Function and that’s it. But then I was thinking, if I need to use Function for all these simple tasks, what’s the point?

The infrastructure that runs your applications can be nearly as complex as the applications it supports. This complexity generally scales with the resilience of the architecture of your application, the scaling needs, and any security concerns. Thus, successful infrastructure for traditional applications often relies on a comprehensive tooling suite that allows the infrastructure engineers to iterate upon and improve your application’s resources.