The world of cloud computing has been revolutionized by a solution called serverless computing. It has been an absolute joy for developers to use. Before this innovation, developers had to worry about the resources powering their code. Since the launch of serverless computing, the developer’s focus on operating-system and hardware architecture is now a thing of the past. It handles all the server management while focusing on what you do well — writing good quality code.

This article will cover how the health of your serverless application can be measured and improved. Technology and its implementation methodology evolve with time very rapidly. Cost efficiency and productivity are the key drivers of technological evolution these days. With the advent of the cloud, infrastructure costs have been brought down significantly. Serverless technology adds icing to the cake!

In the real world, when we implement systems, there are often many kinds of users, such as super business users and some level 1 support technicians who understand the application domain and can support a lot of your applications. Still, due to limited skills and experience with Azure, they are constrained to how much of the application they can help. A great example of this is when you have a solution that uses Azure Service Bus.

Observability is a measure of how well the internal state of a system can be inferred from its external outputs. It helps us understand what is happening in our application and troubleshoot problems when they arise. It’s an essential part of running production workloads and providing a reliable service that attracts and retains satisfied customers.

At Lumigo. we believe in serverless technology, and our mission is to make serverless development easy and fast. For the past few months, we’ve been extending our observability and debugging capabilities, making it a breeze for developers to understand the end-to-end story of every request that goes through the system, find the root causes of issues and be able to easily address them.

Today we are announcing an additional $29 million in funding to help Lumigo grow and provide the same powerful observability capabilities we brought to serverless to other cloud-native technologies, including containers and Kubernetes. Lumigo was founded by Aviad Mor and me a few years ago because we believed the world would be rapidly moving to cloud-native architectures and that these technologies are transformative. Our goal was to create the tools that help developers realize this vision.

Serverless development has been turning heads in the market for quite some time now. But it has yet to be accepted by the majority in the development community. With AWS Lambda, Azure Functions, and IBM’s Open Whisk, the market is poised to take a different route in this field. Most of these organizations are spending a lot of money to make the market accept this new paradigm using serverless computing.

Being a developer is awesome. Writing code, solving problems, and thinking of ingenious solutions for complicated algorithms is what we live for. But, the grass is not always so green on this side of the fence. Sooner or later, you need to get your hands dirty and deploy the app you worked so hard on. Deployments are not always easy. To be blunt, they can be challenging and time-consuming. That’s what we’ll solve in this tutorial.

Azure App Service is a platform-as-a-service (PaaS) offering for deploying applications to the cloud without worrying about infrastructure. App Service’s “serverless” approach removes the need to provision or manage the servers that run your applications, which provides flexibility, scalability, and ease of use. However, App Service also introduces infrastructure-like considerations that can impact performance and costs.

With over 200 products offered by AWS, when designing a solution, such as a micro-services based system using a number of these services at its core, it becomes rather challenging to not only monitor them but on the onset of a problem troubleshooting it and resolving it within the least amount of time becomes a daunting task. Building a monitorable system requires a deep understanding of the failure domain of the critical components, which is a tall order for a fairly complex system.