Artificial intelligence for IT Operations (or AIOps) has been playing an expanding role in helping SREs, DevOps, and developers effectively navigate the challenges around application and infrastructure complexity, pace of change, and data volume that characterize the operations landscape.
The debate between single vendor solutions and best of breed approaches has been ongoing for decades in the technology industry. Engineers have always sought out options and choice, and this has led to a shift in the dominance of large vendors in each stage of technological development. As soon as IBM sold enterprises the mainframe solution, engineers started to look for other options.
Before Megaport, MetTel’s customers battled impaired application performance and deployment delays when moving to the cloud. Now, their connectivity enables high-performance applications in the most distributed cloud environments. Here’s how.
Application performance monitoring (APM) involves a mix of tools and practices to track specific performance metrics. Engineers use APM to monitor and maintain the health of their applications and ensure a better user experience. This is crucial to high quality architecture, development, and operations, but it can be difficult to achieve in Kubernetes since the container orchestration system doesn’t provide an easy way to monitor application data like it does for other cluster components.
In this article, we will dive into Kubernetes network monitoring and metrics, examining these concepts in detail and exploring how metrics in an application can be transformed into tangible, human-readable reports. The article will also include a step-by-step tutorial on how to enable Calico’s integration with Prometheus, a free and open-source CNCF project created for monitoring the cloud.
Network outages happen more often than you think. We may not experience them directly or even know they're occurring at all. When outages affect household names like Facebook, Amazon, Microsoft, and others, however, we're sure to find out after the fact that there was an issue. Depending on the user's activities and the duration of the issue, stress and frustration levels can vary. When a marketer can’t get that ground-breaking advertisement up on Facebook, they can get antsy.
In today’s fast paced and constantly evolving digital landscape, observability has become a critical component of effective software development. Companies are relying more on and using machine and telemetry data to fix customer problems, refine software and applications, and enhance security. However, while more data has empowered teams with more insights, the value derived from that data isn’t keeping pace with this growth. So how can these teams derive more value from telemetry data?
