Systems | Development | Analytics | API | Testing

This Eckerson Group report gives you a good understanding of how the Unravel platform addresses multiple categories of data observability—application/pipeline performance, cluster/platform performance, data quality, and, most significant, FinOps cost governance—with automation and AI-driven recommendations.

Data observability is the ability to monitor and understand the data that flows through an organization's systems. Organizations can monitor their data in real-time, detect anomalies, and take corrective action based on alerts. Organizations use data observability to collect, analyze, and visualize data from various sources to manage their system's behaviour across the data ecosystem.

Data teams and their business-side colleagues now expect—and need—more from their observability solutions than ever before. Modern data stacks create new challenges for performance, reliability, data quality, and, increasingly, cost. And the challenges faced by operations engineers are going to be different from those for data analysts, which are different from those people on the business side care about. That’s where DataOps observability comes in.

The five things you need to know about data observability in a data pipeline are: Becoming a data-driven organization is a vital goal for businesses of all sizes and industries—but this is easier said than done. Too many companies fail to attain the fundamental principle of data observability: knowing the existence and status of all the enterprise data at their fingertips.

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.