Observability Summit North America 2026

Telemetry signals traverse long pipelines before reaching observability backends. While enrichment, filtering, and redaction provide clear value, significant compute cost often comes from repeated conversion through different data formats.
Telemetry commonly flows through SDK formats, wire protocols, collector‑internal formats, and backend ingestion schemas. Each boundary introduces marshaling, unmarshalling and copying. These transformations add no new information, yet consume CPU and memory and scale linearly with volume—creating a hidden "transform tax" that compounds dramatically at terabyte scale.
This talk will share results from measuring instrumented OpenTelemetry SDK and Collector pipelines. We quantify compute spent on pure format conversion versus value‑generating processing and show how these costs grow with scale.
Attendees will learn about conversion costs and strategies to reduce waste: eliminating unnecessary translations, aligning pipeline representations, leveraging zero‑copy techniques, and minimizing transformation hops between pipeline stages. We also examine Apache Arrow‑based representations as one approach to reducing this overhead.

The OpenTelemetry Collector is powerful, but the "debug exporter" only shows raw output. What if you could see your OpenTelemetry logs - with structure, filters, and context - right in your terminal?

This talk introduces Gonzo, an open-source, OTLP-native terminal UI that visualizes logs from the Collector or any OTLP-capable source in real time. Learn how to validate both source instrumentation, and Collector pipelines - including components like filelog, k8sattributes, and transform - without a backend.

Whether debugging, testing configs, or teaching OTel, Gonzo offers a faster, clearer way to understand your telemetry as it flows.

Key Takeaways:
- Validate source instrumentation and Collector pipelines end-to-end
- See enriched OTel logs with structure and context in the terminal
- Debug and iterate on OTel configs faster - no backend required

What happens when a system degrades under high load while all internal metrics remain “green”? At hyperscale, supporting on-demand services across Southeast Asia’s most populous countries, a team observed up to a 7% drop in message delivery. The root cause was not application code, messaging brokers, or load balancers, but a hidden limitation deep within a cloud provider’s firewall.

This war-story session presents a forensic investigation into a managed cloud load balancer and its interaction with connection-tracking tables. The talk walks through the production cutover that triggered the issue and the targeted load testing that ultimately isolated the failure to cloud infrastructure behavior invisible to standard monitoring.

Beyond root cause analysis, the session focuses on outcomes: how sustained, evidence-based debugging led the cloud provider to acknowledge the issue—initially labeled a “limitation”—and introduce a new observability metric, firewall/connections_tracked. Attendees will leave with a practical framework for debugging black-box cloud failures and identifying the node-level metrics needed to detect silent network drops before they impact users.