Observability is a hot term in the industry, but don’t let it fool you: having visibility into your organization's apps and services only gives you partial clarity into a system’s overall performance. To get a full understanding of your monitoring data, you need to apply contextual intelligence.

Do you also find yourself confused by all the Open-this and Open-that names flying around? There are currently a good few Open projects, standards, tools – OpenTelemetry, OpenTracing, OpenCensus, OpenSearch… heck, even my podcast is called OpenObservability! And new Open names seem to be popping up every other day. If you too feel this way, there’s no need. Many feel similarly confused.

The end-to-end monitoring of complex software systems is difficult, toil-intensive and error-prone. Developers, SREs and Platform teams must continuously invest effort in setting up and maintaining the monitoring setups that underpin the observability of their systems, or accept the risk of being unaware of ongoing issues and their impact on end users. Enter model-driven observability powered by Juju!

If there’s one thing folks working in internet services love saying, it’s: "Yeah, sure, but that won’t scale." It’s an easy complaint to make, but in this post, we’ll walk through building a service using an approach that doesn’t scale in order to learn more about the problem. (And in the process, discovering that it actually did scale much longer than one would expect.)

Rich Anakor, chief solutions architect at Vanguard, is on a small team with a big goal: Give Vanguard customers a better experience by enabling internal engineering teams to better understand their massively complex production environment—and to do that quickly across the entire organization, in the notoriously slow-moving financial services industry. They also had a big problem: The production environment itself.