Data streaming is important for getting insights in real time and reacting to events as fast as possible. Its application is wide, from banking transactions and website click analytics to IoT devices and motorsports. The last example represents a really interesting use case.
Many of us get sentimental about past projects we’ve worked on…for me it is a mobile dashboard that leveraged ML/AI to help a sales team make quicker decisions while in the field (nerdy, I know…but it was one of my first projects as a UX Designer when I was starting out my career, and I have many fond memories about this project). For many members of the team at Grafana Labs, that sentimental project is worldPing.
Recently I got the chance to attend a PromQL training conducted by Julius Volz, the co-founder of Prometheus, and in this post I am going to share some of the key takeaways from my experience.
January 20, 15:01 UTC. I was sitting in my home office, watching the screen and feeling a mix of emotion and nostalgia as a pod was getting terminated. We have thousands of pods, continuously starting and terminating, and I’m definitely not spending my days watching them, so why was this one special? The terminating ingester-0 pod was the very last Cortex ingester running on chunks storage in Grafana Labs’ infrastructure.
We recently launched synthetic monitoring, which helps you understand your users’ experience and improve website performance by proactively monitoring your services. This feature, which surfaces the powerful capabilities of Prometheus blackbox exporter, is the next iteration of worldPing.
The Raspberry Pi is a popular and inexpensive device that comes in many shapes and forms. It’s a popular hobbyist tool that is generally purchased to run all kinds of software experiments on. But make no mistake, even though a Raspberry Pi comes in a tiny form factor, it’s a fully functional computer!
Most people are instrumenting their applications, with logs being an easy first step into the observability world, followed by metrics. Tracing lags behind these two and is maybe a little less used than other observability patterns. We hope to change that.
Are you putting the final touches on your plugin before you submit it to the Grafana plugin page? In this article, I’ll share a few tips for how to add that extra polish to your plugins. This article assumes that you already have some knowledge of building plugins for Grafana. If you’re looking to build your first plugin, start by following one of our plugin tutorials.
Three years ago, Tom Wilkie and Frederic Branczyk sketched out the idea for Prometheus monitoring mixins. This is a jsonnet-based package format for grouping and distributing logically related Grafana dashboards with Prometheus alerts and rules. The premise was that the observability world needed a way for system authors to not only emit metrics, but also provide guidance on how to use those metrics to monitor their systems properly.
