Operations | Monitoring | ITSM | DevOps | Cloud

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.

Today’s digital businesses are more interconnected than ever. Industry research shows that 74% of organizations now take an “API-first” approach, and the average application is powered by between 26 and 50 APIs. While this accelerates innovation, it also introduces new risks: when an external provider fails, the impact can be immediate and far-reaching.

Last night, over dinner in Canary Wharf, I found myself catching up with a former colleague who is now leading investments at one of the big banks.

Observability is essential for maintaining and scaling modern applications. With.NET 8, Microsoft has enhanced support for observability using OpenTelemetry. In this post, we explore how to monitor.NET 8 applications logs with SigNoz, an open-source observability platform, using the OpenTelemetry Protocol (OTLP) exporter.

Node.js powers some of the fastest-growing web applications, but its single-threaded nature makes it vulnerable to memory leaks and CPU spikes. To keep your app running smoothly, especially in production, you need more than just web server logs — you need complete visibility across the entire stack.

In one of our previous blogs, we discussed distributed tracing in depth. We examined why distributed tracing is critical and its components - spans and trace context. You can check the complete guide here: What is Distributed Tracing and How to Implement it with Open Source? Here, we'll look at some of the best distributed tracing tools. We'll see what each of them offers so that you can choose the right tool for your monitoring and observability requirements.