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The latest News and Information on Log Management, Log Analytics and related technologies.

Taming Log Noise With the OpenTelemetry Collector's Drain Processor

Do you receive 50 million log lines per day and struggle to see what actually matters? Health checks, heartbeat pings, connection pool messages—they all drown out the errors and anomalies you're trying to find. Most teams deal with this by writing filter rules to drop the noisy patterns. But those rules are manual, per-pattern, and brittle. A new deployment changes a log format and the filter misses it. A new service starts logging a chatty startup sequence nobody thought to exclude.

Real-Time Database Monitoring: Solving Database Latency with Zero-Code eBPF Tracing

In high-throughput database environments, a latency spike is rarely a simple story. Modern data layers are distributed, stateful, and constantly changing as shards move, nodes rebalance, caches warm, queries evolve, and connections churn. In practice, spikes usually come from one of three places: For many SRE and Platform teams, the real challenge is disconnected tooling. As one engineering lead recently shared during a technical workshop: “It’s all disconnected.
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Understanding the Three Pillars of Observability: Logs, Metrics and Traces

Many people wonder what the difference is between monitoring vs. observability. While monitoring is simply watching a system, observability means truly understanding a system's state. DevOps teams leverage observability to debug their applications, or troubleshoot the root cause of system issues. Peak visibility is achieved by analyzing the three pillars of observability: Logs, metrics and traces. Depending on who you ask, some use MELT as the four pillars of essential telemetry data (or metrics, events, logs and traces) but we'll stick with the three core pillars for this piece.

Bindplane Now Ships With a Native AI Skill - Bring Your Own Agent

Today we're rolling out the Bindplane AI Skill, a built-in capability of the Bindplane CLI (v1.98+) that teaches your favorite AI coding tool how to work with Bindplane — natively, accurately, and without the setup headaches of traditional integrations. Read Part 2 of the Bindplane AI Skill series to learn more about how we built it and how it works with real-life examples.

Moving On From MCP: How We Built the Bindplane AI Skill

If you've spent any time wiring AI coding agents into developer platforms over the last year, you've probably reached for MCP. We did too. And after enough sessions watching context windows balloon and tool calls misfire, we started looking for something different. This is the story of what we built instead — a native AI skill for the Bindplane CLI — and the engineering decisions behind it.

Your Team is Using Claude Code. Do You Know What It's Costing You?

The first two weeks of Claude Code are exciting. The third week is when you realize you don’t have visibility into what it’s doing or what it’s costing you. You would not run a production service without metrics, logs, and dashboards or deploy an API without knowing its latency, error rate, or cost per request.

Coralogix and Atlassian: Full-Stack Observability Inside the Incident Workflow

Incident response has a well-known efficiency problem. The tools teams use to detect and investigate issues are often disconnected from the tools they use to manage and resolve them. Engineers spend a significant portion of each incident switching between platforms, assembling context that should already be at hand. Even when the data is available, correlating signals across user, app, infrastructure, and security events to pinpoint a root cause remains manual and slow.

From Vibes to Signals: Observing Your AI Coding Workflow

Agentic coding tools like Claude Code and Codex have taken centre stage and inserted themselves into the critical path of software development. This shift has happened fast, and for most teams, the visibility hasn’t caught up. Until now we’ve been evaluating our vibe coding the same way – on vibes. You might say “this feels faster” or “that seems like a better approach”. That’s not going to scale.

LiveTail: Real-Time Visibility for Active Telemetry

See how Mezmo LiveTail helps teams move from passive log search to active, real-time investigation. In this demo, you'll watch live telemetry stream across services and environments, identify emerging issues as they happen, and use real-time context to troubleshoot faster before signals are delayed, buried, or lost in the noise. LiveTail is part of Mezmo's Active Telemetry platform — built for platform engineers and SREs who need immediate visibility into what's happening across their stack right now, not after the fact.

How Mezmo Uses Active Telemetry for Faster AI Root Cause Analysis

AI-powered root cause analysis only works when the data going into the model is clean, relevant, and structured. In this demo, we show how Mezmo's Active Telemetry approach helps engineers and SREs move from noisy application errors to immediate clarity. Using a restaurant ordering application running in Kubernetes, we trigger a database connection pool exhaustion issue and walk through two ways to investigate it with Mezmo.