Operations | Monitoring | ITSM | DevOps | Cloud

Monitoring and analytics have been an issue for Serverless systems since they were invented. While it’s easy to attach an agent like NewRelic or DataDog to a server or container, function monitoring requires a different approach. Serverless applications, where logic is distributed over a large number of functions, attaching agents or wrappers leads to cost increase and development overhead.

From digital transformation to infrastructure optimization, business analytics and data visualization allow IT stakeholders to make sense of complex situations. You can’t improve what you can’t measure, right? More and more, we see IT organizations combining different sources of information to uncover unique insight, allowing them to detect areas of improvement, find new opportunities, optimize processes and gain that competitive edge.

Cloud-native is the ultimate buzzword lately. So, is “cloud-native storage” just an attempt to grab on to this concept, hoping for a little boost? Actually, there is something more to it, and I’ll unpack that here. The premise of cloud-native storage is simple: its native habitat is a Kubernetes cluster. When we design with the assumption that a technology will exist in Kubernetes, we get to look around and see what functionalities already exist in that system.

The ability to access the internal state of your application ecosystem is critical to optimizing your applications and the experience of your users. Elastic Cloud on Microsoft Azure gives you access to Elastic Observability, allowing you to monitor your infrastructure and see how every signal interrelates by utilizing a wide variety of resources that can be deployed in minutes.

‍Kubernetes is an open-source platform that is used for automating computer application configuration, scaling, and deployment. In order to understand the importance of Kubernetes, let us take a step back and understand why this platform was introduced in the first place.

This blog post was written jointly by the teams at Lumigo and Cloudway. It’s a step-by-step guide to AWS Step Functions workflows with full monitoring and troubleshooting capabilities. We will use the “right to be forgotten” GDPR workflow as an example application.

In the context of businesses, the ultimate goal of innovation for technology companies is to drive a profit. This means that even though many software engineering problems can be solved with enough time and budget — just because a business can solve a problem doesn’t mean that they should. Sometimes, even once a team has figured out how to solve an engineering problem, it doesn’t make sense to deploy the solution — because it costs so much to operate.