OpenTelemetry (OTel) is the fastest-growing observability standard in the world.
It defines how applications should produce telemetry:

• ✔ Logs

• ✔ Metrics

• ✔ Traces

• (and soon) ✔ Profiles

OTel works with ANY backend:

• Prometheus

• Elasticsearch / OpenSearch

• Grafana (Loki, Tempo, Mimir)

• Datadog

• Dynatrace

• Azure Monitor

• AWS CloudWatch

• GCP Operations

• Jaeger / Zipkin

• New Relic

This makes OTel the foundation of modern observability.

OpenTelemetry is an open source CNCF standard that defines:

• A common telemetry format

• APIs + SDKs for instrumentation

• A unified collector

• Exporters to any backend

• Automatic instrumentation for major frameworks

OTel is the "universal language" of logs, metrics, and traces.

Semantic conventions ensure uniformity across teams & tools.

docker run -p 4317:4317 -p 55681:55681 otel/opentelemetry-collector:latest

pip install opentelemetry-instrumentation
opentelemetry-instrument python app.py

collector.yaml

exporters:
  jaeger:
    endpoint: "jaeger:14250"

exporters:
  prometheus:
    endpoint: "0.0.0.0:9464"

A company has:

• 40 microservices

• Logs in ELK

• Metrics in Prometheus

• Traces in Jaeger

OTel enables:

• one collector

• one SDK

• unified context propagation

• cross-service correlation

• easy migration to any vendor

OTel simplifies everything.

• What is OpenTelemetry?

• What are SDKs and collectors?

• Why is auto-instrumentation useful?

• Difference between head-based and tail-based sampling?

• How do exporters work?

• What are semantic conventions?

• Design an OTel-based observability pipeline.

• How to correlate logs with traces using OTel?

• How do you reduce cost with sampling?

• Build a multi-cloud OTel pipeline for 500+ services.

• Enforce tracing standards across all development teams.

• Compare OTel Collector vs traditional agents.

What I learned today:
Where OTel fits in my architecture:
Which telemetry type I want to instrument next:
What I want to explore tomorrow: