Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on Observabilty for complex systems and related technologies.

Search Azure Blob data in-place with BYOS for Cribl Lake

See how Bring Your Own Storage (BYOS) in Cribl Lake allows teams to connect directly to Azure Blob Storage and instantly search data in place — without moving, duplicating, or rehydrating telemetry. In this demo, Cribl Product Manager Risk Salsa walks through setup, dataset creation, and how to run fast investigations across your Azure-hosted data using Cribl Search.

Observability Expanding Beyond Infrastructure and Into AI Systems

Observability revolves essentially around understanding infrastructure health. This means that operations teams monitor applications, netwo0rks, database and cloud environments using familiar signals. They use logs, metrics, latency, uptime measurements, and traces. If systems remain available and the performance stays within expected thresholds, the teams have enough visibility to understand whether applications are functioning properly.

Inside the Grafana AI Team Weekly: Guards for AI Observability (May 5, 2026)

This is an excerpt from a real AI team weekly meeting where we talk about the stuff we build and occasionally also demo them! In this one, Principal Software Engineer Sven Großmann shows a new feature he's working on for AI Observability, called "guards". We're showing parts of our team meetings to build in public in some small way and give you a sneak preview of what's to come. But not all features we show may make it to production! You've been warned. :)

Your Microsoft Azure storage, our data lake power: The best of both worlds

The wait is over for Azure-first organizations. Cribl just launched Cribl Lake Bring Your Own Storage (BYOS) for Microsoft Azure, giving you full data lake power without moving a single byte of telemetry out of your environment. Join us to see how you can finally get the flexibility of a modern data lake while keeping your data in Azure.

Why Traditional Observability Breaks Down in Hybrid Cloud Environments

Hybrid cloud has reshaped the way enterprises build, run, and troubleshoot digital services. Applications now stretch across on-premises infrastructure, cloud platforms, regional services, interconnects, and distributed dependencies that change constantly. Operational complexity has expanded with that footprint, yet many observability practices still reflect assumptions from an earlier era of simpler architectures and clearer boundaries. That gap shows up fast during an incident.

The Complete Guide to Observability Pipelines

Modern engineering teams are drowning in telemetry data. A mid-sized Kubernetes cluster running 50 microservices can generate millions of log lines per minute. Add distributed traces, Prometheus metrics, cloud provider events, and application-level instrumentation and you're looking at terabytes of observability data every day. The problem isn't just volume. It's what you do with it.

How we made a SQL query optimization agent 59% more accurate using autoresearch and LLM Observability

Without experiment infrastructure to help you test your LLM applications, every research session starts with the same questions: What have we tried previously? What were the numbers? Which prompt version produced that result? Why did we discard that approach? The answers live in scattered notes, terminal history, and half-remembered conversations. Each handoff between sessions loses context. In practice, iteration can slow down as teams get bogged down in testing and analysis.

Honeycomb Canvas: The Multiplayer Workspace for the Agentic Era

Last week, we launched a major update to Canvas, our investigation workspace. The new Canvas has evolved from an AI co-pilot you chat with to a place where your whole team, human and agent, can work the same problem on the same surface. Auto-investigations begin the moment a trigger, SLO, or anomaly fires. Custom skills encode your team's runbooks so every agent investigates with your team's expertise built in.

Unlock telemetry value with a well-planned data lake

Your SIEM only holds a slice of your telemetry. Your data lake holds the rest. We'll show you how to use that to your advantage for investigations, threat hunting, and reporting. Why your data lake beats your SIEM for investigations – Your SIEM keeps a short window of expensive, filtered data. Your data lake keeps everything. When something goes wrong, that difference matters more than you think Threat hunting without the handcuffs – Hunting across months of data in a SIEM is painful and costly. We'll show you how a well-planned lake makes broad, deep searches practical and affordable.