Operations | Monitoring | ITSM | DevOps | Cloud

We are excited to announce an expansion of our partnership with Embrace to bring mobile observability to our users using open standards like OpenTelemetry. We first worked with Embrace last year when they created a plugin for Grafana that gives mobile teams an easy way to visualize and analyze real-time mobile metrics directly in a Grafana dashboard.

Many organizations use incident response setups that enable them to page on-call personnel via calling or sending a message to a phone number. In this guide, you will learn how to configure such a system by using Grafana OnCall. For practical purposes, we’ll pair it with Twilio, though the same basic workflow should be applicable to other platforms. We will start with a basic setup that uses a phone number in Twilio to both call and send SMS messages to a webhook integration in Grafana OnCall.

Each day, more than 70 million active users sign into Roblox to create, play, and interact with each other through virtual experiences. And regardless of what those experiences are, exactly — adopting a pet, completing an obstacle course, or fulfilling orders at a virtual pizza parlor — the Roblox observability team is dedicated to making them seamless.

Whether they help you tap into external data sources or add a new visualization type to your dashboard, plugins are a powerful way to customize and extend the value of Grafana. There’s a rich (and constantly evolving) ecosystem of Grafana plugins you can choose from today. While some of these plugins are created and maintained by the Grafana Labs team, many of them are contributed by our commercial partners and community members.

In 2020, we introduced the Snowflake Enterprise data source plugin for Grafana, allowing users to seamlessly pull data from the Snowflake cloud-based data storage and analytics service into Grafana dashboards. Available for Grafana Enterprise and Grafana Cloud users, it’s a powerful way to not only query and visualize Snowlake data, but to do so alongside other data sources, so you can discover correlations and other meaningful insights within minutes.

If you are interested in running your own Grafana Alloy cluster for high availability or horizontal scalability, then you’re in the right place. That’s because we’ve already done it with our own agentless exporters system, which allows you to scrape data from providers such as Amazon CloudWatch, without running any applications on your own infrastructure.

I’ve been a user of Grafana OSS for seven years, starting with Grafana 5.0. My, how things have evolved since then. The first time I used Grafana was to monitor a Kafka data pipeline with a bunch of Java Spring Boot microservices and Prometheus to extract metrics. I was amazed how much you could do with Grafana and Prometheus together, and so I always kept Grafana on my short list of places I wanted to put my energy, either as a contributor or by working directly for the company.

Grafana Tempo 2.5 is here with performance improvements, vParquet4 laying the groundwork for new TraceQL features, and additional metrics capabilities! Watch the video above for a highlight of TraceQL metrics, or continue reading to get a quick overview of the latest updates in Tempo. If you’re looking for something more in-depth, don’t hesitate to jump into the Grafana Tempo 2.5 release notes or the changelog.

Grafana dashboards are a great way to combine a lot of technical information into one convenient picture. From time to time, it’s also useful to export data from a particular Grafana visualization to another format, so you can further analyze it and share it with others. In this blog post, we’ll walk through how to export CSV data for any Grafana visualization you use. This makes it easy to get that data into popular spreadsheet applications, such as Microsoft Excel or Google Sheets. =

As we discussed when we rolled out the latest major release of Grafana Loki, we’ve grown the log aggregation system over the past five years by balancing feature development with supporting users at scale. A big part of the latter has been making queries much faster — and that was a major focus with Loki 3.0 too. We’ve seen peak query throughput grow from 10 GB/s in our Loki 1.0 days to greater than 1 TB/s even before 3.0.