Operations | Monitoring | ITSM | DevOps | Cloud

BubbleUp has always been the fastest way to figure out what a group of outliers have in common. Draw a box around a band of slow traces, a cluster of errors, or any set of events you're interested in, and BubbleUp compares that selection to the baseline across every dimension you've sent us. It's how Honeycomb users find the "unknown unknowns" that dashboards can’t show you.

Your largest prospect has asked for your SOC 2 report. The deal sits still until you produce one. Most teams handle the first half of SOC 2 compliance fine. They control access. They run backups. They put changes through approval before anything ships. The second half is what stops them, and that half is proof. Your policy says access gets reviewed every quarter. The auditor wants the dated review, the signature on it, and the same record from eight months ago.

How many findings from your last vulnerability scan have been verified as fixed? For most IT functions, the scan report is easy to produce, and the proof of closure takes far longer to assemble. That difference tends to surface at the worst possible moment, usually an audit or a post-incident review. Vulnerability assessment tools are meant to end that uncertainty. They inspect systems, match what they find against public vulnerability databases, and rank each weakness by how dangerous it is.

You can already send OTel traces to Sentry. Point your OTLP exporter at Sentry’s endpoint, set environment variables, and your spans show up in the trace explorer. Our OTLP setup guide and “You Don’t Need to Pick One” walk you through that. But those spans are islands. You get a trace waterfall in Sentry, sure.

Most organizations don’t experience modernization as a single initiative. It unfolds over months and years, through a series of decisions made as technology shifts, business needs change, and operational demands grow. Teams adopt new capabilities, automate manual work, sharpen visibility, and strengthen security. These efforts look independent, but they share one requirement: a platform foundation that can keep up with continuous change.

OrionIQ’s agentic investigation is now built into Logz.io Open 360 AI. Ask a question and OrionIQ investigates across your telemetry, shows its work as it goes, links every finding back to the exact query behind it, and tells you how much to trust the answer. Today we’re bringing OrionIQ Chat into Open 360 AI. This is the first OrionIQ product to ship inside the Logz.io platform, and it’s the same agent that powers the standalone OrionIQ app, now available right where you already work.

Updated August 05, 2026 Keyword monitoring checks that a specific string is still present in a page or API response on every run, instead of trusting the HTTP status code. It catches the failures that uptime checks sleep through: a deploy that renders an empty template, a CMS entry someone unpublished, a checkout page serving "Something went wrong" with a perfectly healthy 200. In Hyperping, the simple version is a text body assertion on an HTTP monitor and takes about ten seconds to set up.

Guest post by Markus Opolka, Senior Consultant at NETWAYS. Originally published on the NETWAYS blog as “Icinga und Prometheus integrieren” and “Alertmanager-Icinga-Bridge – Ein Signalilo Fork”, combined and adapted for the Icinga blog with permission. Icinga and Prometheus are both great monitoring solutions, however, their focus is different. In this article, we look at how to integrate both monitoring systems and utilize the strengths of both tools.

In July’s episode of This Month in Datadog, Ruxanda Lueck joins Jeremy for a conversation about how you can confidently evaluate and release features that contain AI-generated code. She also discusses her career trajectory from containers to AI, how agentic workflows impact trust during feature development, and the challenges of testing nondeterministic agent behavior.

Python is the native language of AI-assisted development. Models are really good at writing it, and a lot of people are now shipping it without ever having written much Python themselves. The whole thing is really simple. You prompt an app, Claude Code or Cursor produces a working Flask or FastAPI backend, and you’re live in a few hours. However, there’s still a big difference between “it works on my machine” and “it works in production”.