AI SRE Agent Monitors ClickHouse Cluster Health and Posts to Slack
Ingestion stops, and you start reading system tables by hand to find out why. This run checks disk, merges, and replication across every replica first.
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The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.
Fleet Monitoring with Netdata
Modern infrastructure doesn't only live in data centers anymore. It's in retail stores, factory floors, vehicles, cell sites, kiosks, and robots, thousands of nodes across hundreds of locations, connected over links you don't control, in places your team can't easily reach. Traditional observability wasn't built for this. Centralizing every metric from every device gets expensive fast, degrades over constrained links, and goes blind exactly when a remote node needs attention most. In this webinar, we'll show how Netdata inverts the model by putting intelligence at the edge.
Reading Application Error Logs: Nginx, Apache & System Logs
A quick-reference guide to reading Nginx, Apache, and Linux syslog error lines: what each field means, with real annotated examples. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.
Digital Workspace Monitoring Software Comparison for Enterprise IT
As enterprises adopt hybrid work, IT teams face growing challenges in keeping the digital workspace fast and reliable. Employees access business applications through virtual desktops, SaaS platforms, cloud infrastructure, and physical endpoints, all of which contribute to the overall user experience. Monitoring these environments using disconnected native tools often leaves critical visibility gaps. Digital Workspace Monitoring closes those gaps.
Feature Release: Obkio Insights: Automatic Network Diagnostics (Beta)
Today, we’re launching Obkio Insights, our biggest feature yet, in beta, and the one we’ve been building toward for 8 years. Insights is launching in beta. That means existing customers can start using automatic network diagnostics today, with more root cause coverage, refinements, and improvements rolling out over the coming months based on real-world feedback. As with any beta, you may run into the occasional bug or rough edge.
AI SRE Agent Audits Runbook Coverage and Opens the PRs: AURA
3 a.m., the pager fires, and the runbook describes a service that shipped three versions ago. Ask the agent what the cluster actually has instead. Runbooks go stale because clusters change faster than documentation does. Every deploy, every new service, every renamed alert widens the gap between what is running and what is written down.
How to Survive SOX Compliance Season Without Rebuilding Your Records
Why does SOX season turn into a hunt for screenshots and forwarded approval emails? The controls were almost certainly running all year. The record of them running is scattered across a ticketing tool, an identity directory, a backup console, and someone's inbox. SOX compliance puts financial reporting under a legal standard, and the IT team ends up carrying a large share of the proof. Change approvals, user access lists, backup jobs, and batch schedules all become audit evidence.
How Does a Telemetry Pipeline Work?
Telemetry passes through several stages before anyone can use it. Searching it, charting it, and alerting on it all come later. Each stage makes one decision about the data. Their order separates a pipeline that saves money from one that adds a hop. Most teams meet this layer late, usually after a monitoring bill jumps. Here is how a telemetry pipeline works, stage by stage: By the end you can map your own telemetry flow against the five stages, and see which one is costing you.
How to evaluate session replay software: a developer's guide
Session replay software lets you see exactly what your users saw without having to pull a Freaky Friday with your customer. The question is: which tool actually delivers on that for your team, your stack, and your regulatory environment?
How Datadog saves over $1 million each month by optimizing AI usage
At Datadog, we want to expose our engineers to high-quality AI tools and workflows. However, token usage can be expensive, and finding a balance between AI cloud spend and the return on investment can be difficult. But what if engineers could maintain their current AI workflows using the same tools, but at a lower cost?