While in the past, businesses used data to gain an edge over their rivals, in today’s competitive environment, data is imperative to stay in business. Modern businesses rely increasingly on data to manage all aspects of their operations, from everyday workflows to impacts on business strategy and customer interactions. As a result, data stacks have become extremely complex.

As a Solutions Engineer here at Kong, one question that frequently comes across my desk is “how can I transform a Kong logging plugin message into a format that my insert-observability-stack-here understands, i.e. ELK, Loki, Splunk, etc.?” In this blog, I’m going to show you how to easily accomplish converting a Kong logging payload to the Elastic Common Schema. In order to accomplish this task, we’re going to be running Kong Gateway in Kubernetes and using two Kong plugins.

You have been putting in the work, and your company has been growing manifold, Your client base is growing more than ever, and the projects are pouring in. So what comes next? it is now time to focus on the data that you are generating. When programming an application, DevOps engineers keep track of many things, such as bugs, fixes, and the overall application performance. This ensures that the application operates with minimum downtime and that any future errors can be predicted.

Recently, conversations have been increasing around OpenTelemetry; it is gaining more and more momentum in Node.js development circles, but what is it? How can we take advantage of the key concepts and implement them in our projects? Of note, NodeSource is a supporter of OpenTelemetry, and we have recently implemented full support of the open-source standard in our product N|Solid. It allows us to make our powerful Node.js insights accessible via the protocol.

Unravel now pulls in data quality checks from external tools into its single-pane-of-glass full-stack observability view.

I was chatting with Sanjeev Mohan, Principal and Founder of SanjMo Consulting and former Research Vice President at Gartner, about how the emergence of DataOps is changing people’s idea of what “data observability” means. Not in any semantic sense or a definitional war of words, but in terms of what data teams need to stay on top of an increasingly complex modern data stack.

As a cloud service provider, observability is a critical subject as it's strongly related to the availability of the services running on the platform. We need to understand everything that is happening on our platform to troubleshoot errors as fast as possible and improve performance issues. A year ago, while the platform was still in private beta, we faced a tough reliability issue: users were facing random 500 errors when accessing their applications.