It’s no secret that application containerization has revolutionized the digital world as we know it by providing a transient gateway into elastic infrastructure that can scale and grow as needed. Where traditional virtualization was all about creating a single homogenous entity, containers are self-contained units of software, able to run in just about any environment, making them extremely portable.

You’ve probably seen Rush Hour, a logic puzzle where you have to slide cars and trucks out of the way to steer the red car towards the exit. In real life, when your customers are responsible for tracking hundreds or thousands of data points from dozens of valuable, mission-critical sensors, you’re tracking engine speed, network signal level, distance from the RF, and more—and not just through traffic but across continents.

When building systems that need to scale above a certain number of users, we usually can’t stay on one machine. This is where cloud providers like AWS usually come into play. They allow us to rent VMs or containers for small intervals. This way, we can start a few different machines when more traffic hits, and when it goes down later, we can simply turn off our extra capacity and save money. The question is, how does all this traffic get to our new machines? AWS Elastic Load Balancing!

The combination of SNS to SQS to Lambda is a common sight in serverless applications on AWS. Perhaps triggered by messages from an API function. This architecture is great for improving UX by offloading slow, asynchronous tasks so the API can stay responsive. It presents an interesting challenge for observability, however. Because observability tools are not able to trace invocations through this combination end-to-end. In X-Ray, for example, the trace would stop at SNS.

Over the last few years, Microsoft has built excellent tooling around different technologies. Today, everything is available and achievable through the Azure Portal, which helps manage complex solutions. However, this brings in challenges when it comes to managing the distributed resources. The struggle to keep track of messages or know the flow of messages through these distributed resources is growing day by day.

There are numerous types of services and processes in place to make software development more accessible and efficient for programmers. One ready-for-launch service developers can access remotely is Function as a Service, abbreviated as (FaaS).

Serverless backends have different monitoring challenges when compared with traditional applications, mostly due to the distributed and proprietary nature of serverless. Making monitoring and debugging efficient for serverless requires a unique set of tools and techniques. In this article, we’ll discuss the challenges of debugging serverless backends and how to utilize third party tools to improve the monitoring process.

When you build a cloud-based application, you can choose to deploy the resources using the GUI (Graphical User Interface) or CLI (Command Line Interface) provided by the cloud provider. This approach can work well with just a handful of resources, but as the complexity of your application increases, it can become difficult to manage the infrastructure manually.