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.
Companies are always looking for transparency and visibility when it comes to monitoring, but as monitoring requirements and methods evolve, it’s not always easy to keep up. That’s why Opsdis, an observability consulting company based in Göteborg, Sweden, was founded. The firm focuses solely on helping clients implement systems for monitoring and metrics so they can keep up with the ever-expanding world of cloud computing and containerized environments.
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.
