The Sched app allows you to build your schedule, but is not a substitute for your event registration. You must be registered for Observability Summit North America 2026.
Please note: This schedule is automatically displayed in Central Daylight Time (UTC -5). To see the schedule in your preferred timezone, select from the drop-down menu located at the bottom of the menu to the right.
The schedule is subject to change.
Sign up or log in to add sessions to your schedule and sync them to your phone or calendar.
Today, observability platforms can process massive volumes of telemetry, but practitioners struggle to determine what matters during incidents, unnecessarily increasing usage bills.
This talk resolves the question: “Which telemetry data should we keep?” Learn how one team achieved 30% log reduction by flipping the script and asking “what did we actually use?” instead of “what should we collect?” They conducted a forensic audit of incident resolutions to find receipts proving which data sources truly mattered.
You’ll learn techniques for tracing backward from resolved incidents to identify which telemetry is deemed valuable and see how to map incidents to telemetry data that enabled resolution, revealing which sources proved critical, redundant, or unused.
Using OpenTelemetry (OTel) and Vector, an open-source tool for building fast and scalable observability pipelines, this approach provides a replicable pattern that the community can adapt across different environments.
You’ll leave with a framework for measuring telemetry value based on usage patterns, plus a repeatable audit process. The key question: “Where are the receipts?”
We see IoT everywhere, from smart fridges to air quality sensors, but what about applying observability to billions of living things? Introducing Meowy, my virtual cat with a full observability stack. In this talk, I'll build a digital pet from scratch in Go, instrument it with OpenTelemetry, and visualize its "life" in real time, live-tracking its habits, moods, and (attempted) escapes.
I'll show how to create a RESTful "cat API," instrument it for tracing, and set up alerting with the ELK stack and Kibana visualizations. We'll cover observability basics (logs, metrics, and traces), how to apply them to our digital pet, how to structure telemetry data for "living" systems using AI tools, and how to query all our cat stats with an MCP-connected AI agent. By the end, we'll calculate the average MPH (meows per hour) and expand our understanding of observability applications. No prior observability experience required—just some Go basics and a love for any living thing, from feline to fungal!
