Cloud-native systems generate massive volumes of telemetry signals for metrics, logs, and traces, but more data does not always improve observability. Many teams struggle with noisy signals and poorly designed pipelines that increase storage costs and flood engineers with low-value alerts.
In this session, we focus on providing best practices for building effective, noise-free telemetry pipelines using OpenTelemetry Collector, with the LGTM stack (Loki, Grafana, Tempo, and Mimir) as the backend observability platform. We will demo collectors and design pipelines using processors such as filter, transform, and attributes, along with OTTL (OpenTelemetry Transformation Language) for fine-grained filtering and signal transformation. We will also demonstrate tail-based sampling for traces and routing processors to selectively direct telemetry to appropriate backends, helping control signal volume and storage cost.
By the end of the session, attendees will gain practical experience designing OpenTelemetry pipelines that prioritize signal quality over quantity using various filtering and processing techniques
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