Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes is a fantastic tool for building large containerised software systems in a manner that is both resilient and scalable. But the architecture and design of Kubernetes has evolved over time, and there are some areas that could do with tweaking or rethinking. This post digs into some issues related to how image tags are handled in Kubernetes and how they are treated differently in plain Docker.

DevOps, Observability, Continuous Delivery, Test in Production, Chaos Engineering, and Software Ownership are all major themes in software development today, but why? In an ideal world, we get everything right the first time, nothing breaks, no one DDOS’ us, and the weather report is “Cloudy With A Chance of Meatballs.” Reality of course is different – and better, to be honest.

One of the biggest software development shifts of the past decade has been the emergence of the DevOps movement. There is a growing demand for DevOps Engineers, and organizations that have embraced DevOps practices are becoming more efficient in iterating through service delivery.

Kubernetes clusters can manage large numbers of unrelated workloads concurrently and organizations often choose to deploy projects created by separate teams to shared clusters. Even with relatively light use, the number of deployed objects can quickly become unmanageable, slowing down operational responsiveness and increasing the chance of dangerous mistakes.

There were some big IT headlines this past year. Microsoft acquired GitHub and IBM bought Red Hat. Kubernetes graduated from the CNCF incubator program. And the biggest headline of all—at least to those of us at Datadog, where we live and breathe monitoring—we released Datadog Agent version 6, a completely new monitoring agent written in Go! As we start the new year, we’d like to take a moment to recognize some of the incredible things our engineers accomplished in 2018.

This post is going to be a tad different and longer than what you are used to but I promise, it’s going to be an interesting one. We are going to build a serverless React + GraphQL Web app with Aws amplify and AppSync.

With AWS Lambda, we get scalability and resilience out-of-the-box. What’s more, AWS also provides built-in monitoring, logging and tracing support through CloudWatch and X-Ray. These built-in tools provide a good starting point but many developers eventually outgrow them as their serverless application becomes more complex. In this post, let’s take a serverless application and see how Dashbird can help you debug a serverless application.