How to navigate telemetry through zooming in and out

Observability practitioners can harness telemetry data to analyze system performance at both high-level and granular detail, using New Relic Query Language (NRQL) on metrics, events, logs, and traces collected from various sources like New Relic agents and OpenTelemetry. By employing a technique referred to as "zooming in," users can start with a broad overview and then delve into specific data aspects, such as identifying customers experiencing slow checkout responses in an ecommerce application. The process involves using NRQL to query tracing telemetry, like distributed trace summaries and span events, to pinpoint latency issues and service dependencies, ultimately using these insights to improve system performance. The example presented focuses on an ecommerce application with a frontend service and backend microservices, demonstrating how to trace latency issues in the checkout service and its endpoints over time, highlighting the importance of understanding transaction durations and service dependencies in observability practices.