The scenario: you want to see distributed traces, maybe for your web app. You’ve set up an OpenTelemetry collector to receive OTLP traces in JSON over HTTP, and send those to Honeycomb (how to do that is another post, and we’ll link it here when it’s up).

Honeycomb Play is an interactive sandbox that lets users explore Honeycomb’s data-enriched UI through a guided scenario. The hands-on experience takes a deep dive into how Honeycomb enables you to identify issues, assess their impact, and diagnose their causes for remediation. There is no requirement to sign up—simply dive in and get started right away!

In the race to create more sophisticated apps, microservices and scalable cloud environments, monitoring is not enough. As environments gain complexity, an observability mindset is paramount in transforming data into mission-critical insights.

This is how the developer story used to go: You do your coding work once, then you ship it to production—only to find out the code (or its dependencies) has security or other vulnerabilities. So, you go back and repeat your work to fix all those issues. But what if that all changed? What if observability were applied before everything was on fire? After all, observability is about understanding systems, which means more than just production.

Power has a way of flowing towards people managers over time, no matter how many times you repeat “management is not a promotion, it’s a career change.” It’s natural, like water flowing downhill. Managers are privy to performance reviews and other personal information that they need to do their jobs, and they tend to be more practiced communicators.

Businesses today need to react instantly to changes or alerts that impact the digital experience. Full-stack visibility can help.

Cloud native and microservice architectures bring many advantages in terms of performance, scalability, and reliability, but one thing they can also bring is complexity. Having requests move between services can make debugging much more challenging and many of the past rules for monitoring applications don’t work well. This is made even more difficult by the fact that cloud services are inherently ephemeral, with containers constantly being spun up and spun down.

How do you pass context from events that concern Security teams to Development teams who can make changes and address those events? Often this involves a series of meetings and discussion that can take days or weeks to filter down from security event to developer awareness. Compounding the problem, developers generally do not have access to Splunk Core, Cloud or Enterprise indexes used by security teams, and indeed, may use only Splunk Observability for their metrics, traces and even logs.

If you were pulled into a meeting right now and asked to give your thoughts on how to achieve better outcomes with monitoring and observability, what would you recommend? Would you default to suggesting that your team improve Mean Time To Detect (MTTD)? Sure, you might make some improvements in that area, but it turns out that most of the opportunities lie in what comes after your system detects an issue. Let’s examine how to measure improvements in monitoring and observability.