The raw data, logs, metrics, traces, events, that a system emits about itself, the input layer for all of observability.
Telemetry is the umbrella term for every signal a running system emits about itself: structured logs, time-series metrics, distributed traces, business events, profiling samples. The telemetry pipeline is the collection, processing, and routing layer that gets those signals from the workload to the observability backend, typically agents on each host, an OpenTelemetry collector for processing and routing, and a backend for storage and query. Telemetry quality (consistent, complete, correctly-tagged) is the gate on every downstream observability technique.
You cannot debug what you do not collect. Most 'we couldn't find the root cause' postmortems trace back to a missing piece of telemetry, an unstructured log, an untagged metric, a service that wasn't tracing. Treating telemetry as a first-class engineering concern, with reviews, schemas, and SLOs of its own, is the difference between an observable system and a system that emits a lot of data without being understandable.
See the part of the platform that handles telemetry in production.