opentelemetry

To set up distributed tracing, configure an OpenTelemetry Collector to receive OTLP data, then instrument your microservices using SDKs or auto-instrumentation to export traces and metrics to the collector endpoint.

The best way to correlate traces, metrics, and logs is using vendor-neutral OpenTelemetry semantics to apply consistent service metadata tagging across your distributed system for unified observability.

You control telemetry data volume and cardinality by configuring a collector pipeline that applies tail-based sampling and cardinality control before exporting data to your tracing, metrics, and logging backends.

Yes, you can instrument existing Kubernetes workloads by using a Kubernetes operator to automatically inject OpenTelemetry instrumentation, avoiding manual SDK setup for your services.

Yes, OpenTelemetry supports practical auto-instrumentation paths for Python and Node.js applications, allowing you to automatically generate traces and metrics without modifying application code extensively.

You need a vendor-neutral observability pipeline to avoid fragmented, vendor-specific telemetry, enabling consistent trace-to-log correlation and unified troubleshooting for latency and failures across microservices.