With the release of the Data Explorer, users can now dig into the data behind any tile and slice, dice and explore as needed. This is perfect for those ad-hoc ‘scratchpad’ style scenarios where you want to get some answers without creating yet another dashboard. A couple of weeks ago, we released an awesome new feature that we haven’t had a chance to showcase yet. It’s called The Data Explorer, and I think it’s pretty cool. Let’s dive in!

This release has the version 0.1.0 and is available via our package repositories. Be sure to check the documentation on how to install it. So what is Icinga Notifications actually? It is not possible to explain every single detail now. We will eventually publish separate articles on our blog, which will go into more detail about topics this post only mentions briefly.

Fragmented insights. Limited visibility. Unreliable solutions. For too long, traditional IT tools have not just failed – they’ve actively sabotaged both IT departments and businesses at large. These antiquated systems drain high-value engineering resources, crush frontline support teams, and turn problem resolution into an endless, futile struggle. In an age where AI reshapes entire industries overnight, clinging to these traditional IT approaches border on organizational malpractice.

Human attention spans are seemingly shorter than ever, and your mobile application users are, unfortunately, no exception. Over 70% of users abandon an app if it’s taking too long, with half of these users waiting no more than three seconds. Even minor delays or errors can lead to significant user drop-off, negatively impacting your app’s success and user satisfaction.

Automated event payloads come in many shapes and sizes. These infinitely different event structures pose a problem for users who want to send them all to the same place to page on-call staff. Unless that on-call solution supports the schema directly, you’re out of luck. While we’re proud of the number of integrations we support today for event sources into on-call, we also think the best number that we should support is infinity.

Progress Flowmon 12.4 addresses the challenges in understanding the need for faster and more efficient data analysis. At Progress, our primary focus is to provide accurate and thorough network data insights to our customers. As our customers' networks grow, the volume of telemetry data expands exponentially. This growth, while beneficial, brings with it the challenge of increasingly longer search times through vast amounts of data.

According to a recent study, failing tests alone cost the enterprise software market an astonishing $61 billion annually. This figure mirrors the vast number of resources devoted to rectifying software failures, translating into about 620 million developer hours lost each year. On average, engineers spend 13 hours to resolve a single software failure, a statistic that paints a stark picture of the current state of debugging efficiency.

We are excited to announce a new enhancement to our platform that will further empower you to stay ahead of potential issues. You can now report an issue for services directly from your StatusGator admin dashboard. Help out other teams who might be experiencing downtime by reporting unpublished issues. These crowdsourced issue reports are one of the datapoints that power our new Early Warning Signal alerts.

We are excited to share a significant update for Grafana Pyroscope users and the broader open source community: the launch of Explore Profiles, a new application that makes it easier and faster to surface meaningful insights from your profiling data. Explore Profiles is a Grafana app plugin designed to integrate seamlessly with Grafana Pyroscope, the open source continuous profiling backend, providing a smooth, queryless experience to browse and analyze your profiling data.

Foundation models, or large AI models, are the driving force behind the advancement of generative AI applications that cover an ever-growing list of use cases including chatbots, code completion, autonomous agents, image generation and more. However, when it comes to understanding observability metrics, current large language models (LLMs) are not optimal.