Operations | Monitoring | ITSM | DevOps | Cloud

The more data you have, the harder it becomes to read through it, let alone identify trends or crucial patterns. Couple that with a shortage of time, and the ability not only to visualize but also to communicate with your data becomes paramount.

We are consistently releasing helpful updates and fun features in Grafana Cloud, our fully managed observability platform powered by the open source Grafana LGTM Stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics). In case you missed it, here’s a roundup of the latest and greatest upgrades for Grafana Cloud this month. If you’re not a Grafana Cloud user, what are we waiting for?

The evolution of public cloud over the past few years has been remarkable. Digital transformation, remote work, and AI have created breakneck growth. Back in 2018, before anyone uttered the words COVID or ChatGPT, there were already big drivers for public cloud. The global digital transformation market size was valued at $320 billion, and set for 18% annual growth, to reach a projected $695 billion by 2025.

Software deployment, a critical process in software development, refers to all the activities that make a software system available for use. It’s the stage where all the hard work of creating software culminates into something tangible that users can interact with. But before we delve into its complexities, let’s first understand the basics of software deployment.

Slow code is everywhere—or maybe "anywhere. "Slowness" is a spectrum. Sometimes, you triumphantly move performance bottlenecks so that the bar for "slow" always gets lower. In our experience, though, things often trend in the other direction.

The term “data mesh” isn’t especially new, but the definition of what it is and does is perhaps not widely agreed upon, so let’s explore it a bit.

In the rapidly changing world of technology, effective monitoring is critical for maintaining your infrastructure and ensuring it performs effectively. While traditional monitoring methods are effective, they can fall short as systems scale and become more dynamic and complex. This article aims to bridge the gap by introducing software engineers to the power of machine learning (ML) in infrastructure monitoring, outlining not just the ‘how’ but the ‘why’ of its application.

Sentry knows a lot about the inner workings of an application’s codebase. So we got to thinking, how can we use this rich dataset to make debugging with Sentry even faster? Many generative AI (GenAI) tools (e.g. GitHub Copilot) improve developer productivity in their dev environment, though few have the contextual data Sentry has to help fix errors in production.

We’ve been exploring new approaches to make Sentry issues more actionable. Read on to learn how we’re using machine learning to improve issue creation (i.e. grouping) and alerts, with as little configuration as possible, so you can focus on fixing the most important issues.

Kentik NMS has the ability to collect multiple SNMP objects (OIDs). Whether they are multiple unrelated OIDs, or multiple elements within a related table, Leon Adato walks you through the steps to get the data out of your devices and into your Kentik portal.