If you’re an experienced engineer, you likely have comprehensive observability and monitoring set up for your production systems. So if issues arise, you’re empowered to resolve them quickly. Yet, there are way too many systems out there, especially smaller and simpler ones, which are running with only rudimentary observability systems, or no observability at all. This means when an application goes down or starts to perform poorly, it may be very hard to pinpoint and resolve the issue.

Lessons from the front lines: Moving to observability maturity What separates the observability experts from the novices? It's a question that's been on my mind lately, especially after diving into our recent 2024 State of Observability Survey of over 500 practitioners. In my past roles as a DevOps engineer and a site reliability engineer (SRE), I've seen firsthand how a mature observability practice can be the difference between sleepless nights and smooth sailing.

Maximize your observability with tracing tools to track requests, identify bottlenecks, and optimize system performance across services.

Managing observability workloads can quickly overwhelm even the most experienced admin. Maybe you’re dealing with multiple departments, each needing its own collector configurations and pipelines. Every time you have to run a test or roll out a change, the process is cumbersome and introduces risk. Or perhaps you’re responsible for tracking hundreds of collectors across different environments and regions. In a scenario like this, troubleshooting individual issues feels nearly impossible.

We’ve been talking about observability 2.0 a lot lately; what it means for telemetry and instrumentation, its practices and sociotechnical implications, and the dramatically different shape of its cost model. With all of these details swimming about, I’m afraid we’re already starting to lose sight of what matters.

The Schrödinger's Cat thought experiment highlights the paradox of determining a system's state without direct observation—an apt analogy for the challenges of observing cloud-native applications. These systems' complex, ephemeral, and distributed nature often makes them appear as black boxes. Coupled with the operational complexities of multi-cloud and hybrid environments, gaining a clear picture feels impossible.

When your system shows a "no healthy upstream" error, it usually means your load balancer can't connect to any of your backend servers. Let's get straight to fixing this problem.

In today’s digital economy, infrastructure has evolved from your organization’s technical foundation to a strategic asset that can make or break your business outcomes. Yet, as companies embrace hybrid environments, many find themselves struggling with a critical challenge: how to maintain control and visibility across increasingly complex infrastructure landscapes and AI workloads.