Operations | Monitoring | ITSM | DevOps | Cloud

It's been one year since we issued an AI mandate inside Honeycomb, and we've been doing a lot of reflecting internally on how far we've come and how far we have yet to go.

Summary This guide walks through an end-to-end Grafana and InfluxDB 3 integration using a realistic dataset you generate yourself. The tutorial covers getting data in, transforming it, connecting Grafana, and building real dashboards. Table of Contents InfluxDB and Grafana are the most common pairing in time series monitoring, and division of labor between them is simple.

We put a blunt question to developers on a recent live webinar: right now, could you actually prove AI agents are paying off for you or your team? Only 24% said yes. The other 76% were guessing, unsure, or already suspicious that agents are costing more than they’re saving. That gap between adoption and proof is the real story in agentic development right now. Teams aren’t behind on running agents. They’re behind on knowing whether it’s working.

If your team works with SAP HANA, the main challenge usually isn’t finding another dashboard. The real difficulty is identifying which tool can quickly help you trace vague complaints about slow transactions to their root cause. In many setups, SAP HANA monitoring is divided among native SAP interfaces, cloud monitoring, infrastructure dashboards, and the broader monitoring systems used by the rest of IT. This fragmented approach can slow down root cause analysis.

Table of Contents For most of Kubernetes’ history, memory management has been a blunt instrument. Cross your limit, and the kernel kills your container. There has been no equivalent to CPU throttling, no graceful backpressure, just a hard stop. With Kubernetes 1.37, that changes: Memory QoS, built on cgroups v2, graduates to Beta and is enabled by default.

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey towards autonomous operations. Read on to learn about how Scoped OAuth for Public Apps, now in Early Access, builds towards this vision. Your security team asks a simple question during a routine review: which third-party apps can reach our PagerDuty data right now, and what exactly can they do with it?

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey towards autonomous operations. Read on to learn about how PD Automation Runner, now in Early Access, builds towards this vision. Your on-call engineer gets paged for a critical alert on a self-hosted Kubernetes cluster. If this were a cloud-hosted service, Workflow Actions and the SRE Agent would already be working the problem: pulling logs, checking health, surfacing a fix.

The ability to log into all corporate applications with one username and password is pretty convenient, even compared to a password manager. As a benefit, the IT department can centrally enforce one desired two-factor auth mechanism. Now we, Icinga, also provide a so-called OpenID Connect integration for single sign-on. By the end of this text you’ll know how to connect your Icinga Web instance to the ID provider of your choice.

Most growing teams eventually consolidate onto a full observability platform, and for teams correlating logs, metrics, and traces across a complex system, that’s often the right call. But a dedicated log tool still wins for a specific set of teams: ones that need to move fast, keep costs simple, and get real answers from logs without carrying the weight of a platform they don’t fully need yet. Here’s when that’s you.