Migrating workloads to the cloud can be tricky. In fact, a study Virtana conducted earlier this year found that 72% of respondents had to move applications back on-premises after migrating them to the public cloud because they ran into a variety of problems. Clearly, organizations need to address these showstoppers.
As a Nexthink V6 Customer, you’re already realizing the power and value of a proactive, Digital Employee Experience (DEX) management solution: more productive employees, reduction in employee issues, and a smoother, more accelerated time-to-resolution for employee-reported IT issues.
How can you run a fully managed Kubernetes in a private cloud at half the cost of Amazon EKS (Elastic Kubernetes Service)?
Migrating your organization’s applications to the cloud is no small task. Before planning and execution can even be fathomed, many of our customers’ first challenge is to create a data-driven business case for management buy-in.
A fundamental element of the Kubernetes microservices system is the services model, which gives teams greater understanding of how their applications are deployed. These objects running within pods and containers, by extension, are RESTful since they’re based on APIs. However, DevOps teams can’t hope to run a tight ship without managing their services. Communication and visibility are absolutely crucial in a Kubernetes system.
From autonomous mobile robots to robot butlers. It is impressive to see how much progress has been made in the last decade. Thanks to our open source robotics community we keep learning, and this newsletter is filled with events, R&D updates, new products and tutorials for you. Thanks July!
In the first post of this series, we covered the general idea and benefits of model-driven observability with Juju, but did not dive deep into the idea of contextualization and how it makes observability more actionable. In this post we start addressing what contextualization means in model-driven observability, starting from adding Juju topology metadata added to telemetry, and how that improves the processing and querying the telemetry for charmed applications.
Inevitably, in the lifetime of a service or application, developers, DevOps, and SREs will need to investigate the cause of latency. Usually you will start by determining whether it is the application or the underlying infrastructure causing the latency. You have to look for signals that indicate the performance of those resources when the issue occured.
We launched the Stanza log agent just over one year ago. Stanza is the result of an uncompromising stance on performance, processing, and configurability for log telemetry. It took mere days for friends and colleagues in the space to raise the obvious objection – there are already so many logging agents, so why spend time on a *new* one? We also heard from competitors who had a snarkier take…
