What is Telemetry?

Telemetry is the data a software system emits about its own behavior, collected and transmitted to a remote system for analysis. The word literally means “measuring at a distance” (from the Greek tele + metron), and it entered software from aerospace: engineers on the ground understanding a spacecraft they can’t inspect directly — exactly the situation of an engineer debugging a production system they can’t attach a debugger to.

Types of telemetry data

In modern systems, telemetry usually means the three pillars of observability:

• Logs — timestamped records of discrete events
• Metrics — numeric measurements over time
• Traces — records of a request’s path through distributed services

Broader definitions also include events, continuous profiling data, real-user-monitoring signals from browsers and mobile apps, and — increasingly — token counts, latencies, and prompt/response records from LLM calls.

How telemetry is collected

Telemetry originates from instrumentation: code (manual or automatic) that records what the application is doing. It is then gathered by agents or collectors, optionally processed in a telemetry pipeline (filtering, redaction, enrichment, sampling), and shipped to a backend for storage and analysis.

The industry standard for all of this is OpenTelemetry (OTel), a CNCF project that provides vendor-neutral SDKs, an agent (the OpenTelemetry Collector), and a wire protocol (OTLP). Instrumenting once with OTel means you can switch backends without re-instrumenting.

Why telemetry strategy matters

Telemetry volume grows faster than traffic: more services, more granular instrumentation, more retained history. Uncontrolled, it becomes a major line item — per-GB ingest pricing is the main driver of surprise observability bills. Good telemetry strategy balances coverage (enough signal to debug anything) against cost (sampling, filtering, tiered retention).

Telemetry in OpenObserve

OpenObserve is an OpenTelemetry-native backend: it accepts OTLP logs, metrics, and traces directly, and its pipelines let you filter, transform, and redact telemetry at ingest. Columnar storage on S3-compatible object stores keeps long retention affordable even at high telemetry volumes.