I have a good sense of how to use traces to understand my system’s behavior within request/response cycles. What about multi-request processes? What about async tasks spawned within a request? Is there a higher-level or more holistic approach?

I can’t remember the last time I drove down highway 101 between San Francisco and the South Bay and didn’t see a billboard claiming to be the single tool to solve all of my data problems.

When you send telemetry into Honeycomb, our infrastructure needs to buffer your data before processing it in our “retriever” columnar storage database. For the entirety of Honeycomb’s existence, we have used Apache Kafka to perform this buffering function in our observability pipeline.

SaaS is exploding and so it should; it takes commoditized work and infrastructure away from tech teams so that they can focus on differentiating features. But what happens when it goes wrong? How do SaaS platforms make sure they aren't letting their customers down and in turn, letting their customers down? Observability, bolstered with AI gives all the partners the best chance to optimize availability and customer experience. Here's how.

Technology environments are rapidly evolving as organizations look to remain competitive, accelerate innovation and make themselves more agile. But in the process, many of the observers, i.e., stakeholders who track infrastructure and application metrics, are falling behind, unable to monitor and manage modern, cloud-native apps and multi-cloud environments due to the complexity that comes with them.

Achieving full, 360-degree observability across your entire IT ecosystem and application components can be thwarted by the disconnect between technical monitoring and business outcomes - running the risk of catastrophic service failures.