Operations | Monitoring | ITSM | DevOps | Cloud

Most internal AI projects for IT operations next exit pilot. Budgets stretch, priorities shift, key hires fall through, and what started as a strategic initiative turns into a maintenance burden—or worse, shelfware. Not because the teams lacked vision. But because building a production-grade AI agent is an open-ended commitment. It’s not just model tuning or pipeline orchestration. It’s everything: architecture, integrations, testing frameworks, feedback loops, governance, compliance.

Linux log files are text-based records that capture system events, application activities, and user actions. They're stored primarily in the /var/log directory and provide essential information for debugging issues, monitoring system health, and maintaining security. This guide covers the most important Linux log files and a few detailed techniques for reading and analyzing them.

Pulling observability data together is rarely clean. Metrics come from everywhere, formats vary, and making sense of it takes some work. OpenTelemetry Collector and Prometheus fit perfectly here. The Collector handles ingestion and processing from different sources, while Prometheus stores and queries the data. Simple, effective, and no vendor lock-in. In this blog, we cover how to integrate the Collector with Prometheus, common pitfalls, and ways to control costs.

In a really broad sense, the history of observability tools over the past couple of decades have been about a pretty simple concept: how do we make terabytes of heterogeneous telemetry data comprehensible to human beings? New Relic did this for the Rails revolution, Datadog did it for the rise of AWS, and Honeycomb led the way for OpenTelemetry.

Migrating to a new logging platform can be a complex operation, especially when it involves both active and historical logs. Observability Pipelines offers dual-shipping capability, making it easy to route active logs to your new platform without disrupting your log management workflows. But migrating years worth of historical logs—which are critical for investigating security incidents and demonstrating compliance with applicable laws—requires a different approach.

Log volume is exploding, costs are rising, and most teams are stuck duct-taping together short-term fixes. During our webinar, "Optimizing Log Management in Datadog: Cut Costs Without Losing Insights," we discuss how DevOps and engineering leaders are navigating the growing pains of observability, especially in environments where tools like Datadog are mission-critical but challenging to manage. Here’s a recap of the key takeaways.

On March 2, 2025, Firefly Aerospace made history. The company — a space services firm that offers safe, reliable, and economical access to space — completed the first fully successful lunar landing by a commercial provider with its Blue Ghost Mission 1. But behind the headlines and highlight reels was a team of dedicated engineers, years of preparation, and a mission control center outfitted with Grafana dashboards.

About 15 years ago, I worked at a company building network security appliances (with ARM-based network processors) and was responsible for the development of custom Linux firmware. The product launch was successful; we shipped and managed a large fleet of devices in the field. After a few firmware releases, we received alerts from the device management system telling us that there were intermittent problems. Remoted into the appliances but could not reproduce the error.