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.
We’re pleased to share that Circonus saw record sales for the quarter ending June 30, 2021 and substantial year-over-year growth in annual recurring revenue (ARR). We’re experiencing significant momentum in 2021 as more organizations look to consolidate monitoring solutions, unify observability metrics across the stack, and manage a significantly greater volume of telemetry data.
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.)
Over the past three years, we have served thousands of developers with our two major products, Thundra APM and Thundra Sidekick – and it still feels like we’re just getting started. We would like to thank all of our users and supporters who gave us the strength to build our one-of-a-kind products. And we are very excited to announce our latest innovation: Thundra Foresight!
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.