Operations | Monitoring | ITSM | DevOps | Cloud

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.

Digital transformations can entail significant shifts in technology, such as migrating from on-site architecture to cloud services, and these complex transformations generate massive amounts of data. Data transparency is a must-have, and observability with AIOps delivers the solution. Through a unified view of data, AIOps guides DevOps and SRE teams through the swamp of information

An application running in production is a difficult beast to tame. Most experienced developers–ones who spent enough late nights or Saturday mornings trying to break apart a nasty production bug–will try and create the clearest possible picture for their later selves while writing their code, so that they could understand what’s actually going on in the system during an incident.

OpenTelemetry (or “OTel”) helps you get your instrumentation started quickly, and it helps you get the most out of that telemetry data by providing flexible exporting options. As a result, it’s emerging as the new standard for instrumentation. To that end, today we’re sharing more insight into the work we’ve done (and are doing) to enable a path for all Honeycomb users toward OTel adoption. We hope you’ll be as excited as we are to embrace these open standards!

Today you likely have one or more legacy APM (Application Performance Monitoring) solutions. You are moving from a monolithic architecture to microservices, and you are accelerating your journey to Cloud, and you need to deliver at speed with scale and quality to your customers. Sadly, visibility into these results are limited to each of these solutions and their interfaces.

You’re likely familiar with local debugging—the ability to go through your code line by line to find and eliminate bugs. However, with the ever-increasing complexity of development environments, working efficiently with remote systems is becoming more necessary. In this case, “remote” can mean any machine you don’t have native OS-level access to, such as Virtual Machines, Docker containers, and entirely separate devices accessed over the network.

“Observability,” is the process in DataOps of recording data generated by digital systems as they go about their processes. There are some great companies in the observability space, generating a whopping $17 billion annually, and contributing a significant portion to the modest 2.5 quintillion bytes of data created every year.