Modern applications built on microservices and cloud-native infrastructure are harder to understand from the inside than monoliths ever were. OpenTelemetry (OTel) is an open-source observability framework that provides a standardized way to generate, collect, and export telemetry data -- traces, metrics, and logs -- from applications and infrastructure. It's a vendor-neutral project under the Cloud Native Computing Foundation (CNCF) and has become the industry standard for instrumenting distributed systems.
OpenTelemetry formed in 2019 through the merger of OpenTracing and OpenCensus, combining their strengths into a single framework. Since then, it has gained widespread adoption, with contributions from major cloud providers and observability vendors. The project provides APIs, SDKs, and tools across dozens of programming languages, making instrumentation practical regardless of technology stack.
Key Components of OpenTelemetry
Traces
Distributed tracing is one of OpenTelemetry's core pillars. A trace represents the full journey of a request through multiple services. Each trace is composed of spans -- individual units of work that capture the operation name, timing, status, and contextual attributes. Traces let engineers pinpoint exactly where latency builds up and where errors occur, even across dozens of microservices.
Metrics
OpenTelemetry's metrics API records numerical measurements about system behavior over time: counters (HTTP request counts), histograms (latency distributions), and gauges (current memory usage). These metrics export to a variety of backends for aggregation, alerting, and visualization.
Logs
While traces and metrics were the initial focus, log support has matured significantly. OpenTelemetry correlates log records with traces and metrics by attaching trace context to log entries. This correlation is one of the framework's most powerful features -- it lets engineers move seamlessly from a high-level metric anomaly to the specific trace and log lines that explain it.
The OpenTelemetry Collector
The Collector is a standalone service that receives, processes, and exports telemetry data. It acts as a centralized pipeline between applications and observability backends. The Collector supports a wide range of receivers (OTLP, Jaeger, Zipkin, Prometheus, and more), processors (batching, filtering, sampling, enrichment), and exporters (for sending data to one or more backends). Running a Collector decouples instrumentation from backend choice, making it straightforward to switch or add backends without touching application code.
SDKs and Instrumentation
SDKs are available for Java, Python, Go, JavaScript/Node.js, .NET, Ruby, Rust, and more. They implement the OpenTelemetry API and handle creating spans, recording metrics, and exporting data. Automatic instrumentation libraries capture telemetry from popular frameworks and libraries -- HTTP clients, database drivers, messaging systems -- with minimal code changes.
OpenTelemetry Backends
OpenTelemetry is deliberately vendor-neutral. It defines how telemetry data is generated and collected, but doesn't store or visualize it. That data needs a storage and analysis backend, and choosing the right one is an important architectural decision.
Observability data -- especially traces and logs -- can be extremely high-volume. Organizations need backends that handle massive ingestion rates, provide fast queries, and manage storage costs:
ClickHouse has emerged as a leading backend for OpenTelemetry data, particularly traces and logs. Its columnar storage and exceptional compression make it efficient for the large, structured datasets observability pipelines produce. ClickHouse ingests millions of spans per second and runs analytical queries across billions of rows in sub-second time. Projects like SigNoz and Uptrace use ClickHouse as their primary storage layer, and the ClickHouse exporter for the Collector makes integration straightforward.
Elasticsearch is widely used for log storage and analysis in observability stacks. Its full-text search capabilities make it strong for log exploration and ad-hoc querying. Combined with Kibana for visualization, it provides a mature platform for telemetry analysis. The Elastic APM server accepts data directly in OTLP format, making it a natural fit for OpenTelemetry deployments.
OpenSearch offers similar capabilities as a fully open-source alternative. It supports trace analytics and log analytics natively through its Observability plugin and OpenSearch Dashboards. Data Prepper acts as a bridge between the OpenTelemetry Collector and OpenSearch, transforming and indexing traces and logs for analysis.
Choosing and operating the right backend at scale -- configuring ingestion pipelines, optimizing storage, tuning queries, managing retention -- is where deep expertise in these technologies pays off.
Common Use Cases
- Microservices Monitoring: Trace requests across service boundaries to understand dependencies, spot bottlenecks, and troubleshoot failures in complex distributed systems.
- Infrastructure Observability: Collect metrics and logs from servers, containers, and Kubernetes clusters to monitor utilization, detect anomalies, and maintain system health.
- Application Performance Monitoring (APM): Measure and optimize response times, error rates, and throughput for web applications and APIs.
- CI/CD Pipeline Observability: Instrument build, test, and deployment pipelines to find slow stages, flaky tests, and deployment failures.
- Cloud Cost Optimization: Use detailed telemetry to understand resource consumption and right-size infrastructure based on actual usage.
The OpenTelemetry Ecosystem
OpenTelemetry benefits from a broad, active ecosystem. The OTLP specification defines a standard wire format for telemetry data, ensuring interoperability across tools and vendors. AWS, Google Cloud, and Azure all support OTLP natively in their managed observability services.
The project maintains an extensive registry of instrumentation libraries, Collector components, and integrations. Observability platforms -- Grafana, Datadog, Splunk, New Relic, Honeycomb -- all accept OpenTelemetry data, and many contribute directly to the project. This broad support means adopting OpenTelemetry doesn't lock you into any single platform. Your instrumentation investment stays portable regardless of where you store and analyze your data.
OpenTelemetry has reached stable status for traces and metrics across most supported languages, with logs progressing rapidly. As the standard matures, it's increasingly becoming the default way to instrument cloud-native applications -- replacing proprietary agents and fragmented tooling with a single, consistent framework.