Operations | Monitoring | ITSM | DevOps | Cloud

In our increasingly serverless world, observability isn’t just a “nice to have”—it’s essential. Serverless functions such as AWS Lambda bring incredible benefits, but they also introduce complexities, especially around monitoring and debugging. In a previous article, I provided a quick, practical guide for sending AWS Lambda traces to Grafana Cloud using OpenTelemetry.

AI systems, from machine learning models to Large Language Models (LLMs) and autonomous AI agents, introduce unique observability challenges. Their non-deterministic nature, complex dependencies, and specialized performance characteristics require thoughtful instrumentation approaches. OpenTelemetry has emerged as the leading standard for implementing observability across these systems.

Effective database monitoring is essential for maintaining application performance and reliability. This guide explores how to implement OpenTelemetry with PostgreSQL, providing DevOps engineers and Site Reliability Engineers with practical steps for comprehensive database observability.

I was lucky enough to attend KubeCon Europe 2025 in London, where the energy around OpenTelemetry (OTel) reached fever pitch. From packed sessions to buzzing hallway conversations, it’s clear: OpenTelemetry isn’t just the future—it’s the present. Here’s what stole the spotlight.

Intellyx BrainBlog by Jason English for Mezmo ‍ “Bubble bubble, toil and trouble” describes the mysterious process of mixing together log data and metrics from multiple sources as they enter an observability data pipeline. ‍ Customers demand high performance, functionality-rich digital experiences with near-instantaneous response times.

Setting up robust observability for your Spring applications is essential for maintaining reliable, high-performing systems. This guide walks you through implementing Spring OpenTelemetry with practical advice for common challenges.

OpenTelemetry is officially the second-largest project in the CNCF, only behind Kubernetes. This year we had 13.000 engineers, maintainers, and vendors show up in London.

Instead of deploying a patchwork of proprietary agents for every platform, a telemetry pipeline lets you route your data through a single, consistent layer—and send it to any backend you choose. Flexibility, achieved. But there’s a catch. If your pipeline is proprietary, you’ve only shifted the lock-in left. Sure, you can now add or swap destinations freely—but you’re still deeply dependent on a vendor in the middle of your data flow.

Breakpoints and console.log statements might save your sanity during local dev, but production issues are another story. In prod, your errors might be distributed across different microservices, or hidden in minified code. Good luck hunting those down. That’s where Sentry’s traces and spans come in, offering you easy visibility into every network request, API call, DB fetch and more in a full-stack, distributed environment.