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.

Grafana Cloud is the easiest way to get started observing metrics, logs, traces, and dashboards. When we say “easiest,” we mean it: Grafana Cloud is designed so that even novice observability users can use it. As a new user, you are not required to dive into the complexity of setting up Prometheus and figuring out how to create Grafana dashboards from scratch. Integrations are the reason why.

Oftentimes users of open source are told to go download it and figure it out… or pay for a managed solution in the cloud. So the typical choice is free and do-it-yourself or expensive and easy. With our new changes to Grafana Cloud, we are making it both free and easy to have a real, composable observability solution.

In my ongoing Loki how-to series, I have already shared all the best tips for creating fast filter queries that can filter terabytes of data in seconds and how to escape special characters. In this blog post, we’ll cover how to use metric queries in Loki to aggregate log data over time.

You’ve probably heard about Prometheus, the leading open source project focused on metrics and alerting, and how it has changed the way the world does monitoring and observability. But if you’re brand-new to the technology, how can you dip your toes in and get started? I was in this position not long ago myself. I am a very hands-on type of learner, and usually when I want to explore new technologies, I start with “hello world” apps and small toy projects.

Smart State Technology (SST) is a company based in the Netherlands that develops advanced technological and future-proof solutions for smart grids. Their mission is to reinforce critical energy infrastructures by providing innovative energy solutions that connect industry and research, while ensuring society can fully benefit a sustainable energy future.

In my ongoing Loki how-to series, I have already shared all the best tips for creating fast filter queries that can filter terabytes of data in seconds. In this installment, I’ll reveal how to correctly escape special characters within a string in Loki’s LogQL. When writing LogQL queries, you may have realized that in multiple places you have to write strings delimited by double quotes.