In today’s multi-cloud landscape, maintaining smooth and reliable connectivity requires complete visibility into cloud networks. With Kentik, network and cloud engineers gain the tools to monitor, visualize, and optimize Azure traffic flows, from ExpressRoute circuits to application performance, ensuring efficient and proactive operations.

Since the emergence of the cloud, the DevOps movement, and the rise of microservices, developers have been increasingly responsible for the operation of their software. “You build it, you run it” (YBYR) and “You build it, you operate it” (YBYO) have become common mantras in the software engineering industry. However, there’s a misunderstanding in this statement. Developers should remain focused on building software.

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.

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.

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.