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Unlocking Insights with Observability: My Journey with OpenTelemetry

A full stack engineer specializing in DevOps and AI infrastructure uses OpenTelemetry to instrument applications and collect telemetry data for real-time monitoring and performance optimization. The engineer instruments Spring Boot applications with the OpenTelemetry Java SDK and exports data to backends like Prometheus and Grafana for visualization and analysis.

read2 min publishedMay 28, 2026

As a Full Stack Engineer specializing in DevOps, AI Infrastructure, and Cloud, I've come to realize the importance of observability in ensuring the reliability and performance of complex systems. In my experience, having visibility into the inner workings of our applications and infrastructure is crucial for identifying issues, optimizing resources, and improving overall user experience. With the rise of distributed systems and microservices, observability has become more critical than ever.

I use OpenTelemetry to gain insights into my applications and services. OpenTelemetry is an open-source framework that provides a unified way of collecting and managing telemetry data from distributed systems. It allows me to instrument my code, collect metrics, logs, and traces, and send them to various backends for analysis and visualization. With OpenTelemetry, I can monitor my applications in real-time, identify bottlenecks, and optimize performance.

In my projects, I instrument my applications using OpenTelemetry's APIs and SDKs. For example, I use the OpenTelemetry Java SDK to instrument my Spring Boot applications. Here's an example of how I use the SDK to create a span and add attributes to it:

import io.opentelemetry.api.trace.Status;
import io.opentelemetry.api.trace.TraceKey;
import io.opentelemetry.context.Scope;
import io.opentelemetry.context.thread.LocalThreadScope;
import io.opentelemetry.sdk.trace.data.SpanData;
import io.opentelemetry.trace.Span;
import io.opentelemetry.trace.Status;

// Create a tracer
Tracer tracer = OpenTelemetry.get().tracerProvider().get("my-tracer");

// Create a span
Span span = tracer.spanBuilder("my-span").startSpan();
try (Scope ignored = span.makeCurrent()) {
    // Add attributes to the span
    span.setAttribute("key", "value");
    // Do some work...
} finally {
    span.setStatus(Status.OK);
    span.end();
}

Once I've instrumented my applications, I use OpenTelemetry's exporters to send the telemetry data to various backends such as Prometheus, Jaeger, or New Relic. I then use visualization tools like Grafana or Kibana to gain insights into my applications' performance and behavior. For example, I use Prometheus and Grafana to monitor my application's metrics and create dashboards to visualize the data. Here's an example of how I configure Prometheus to scrape my application's metrics:

scrape_configs:
  - job_name: 'my-app'
    scrape_interval: 10s
    metrics_path: /metrics
    static_configs:
      - targets: ['my-app:8080']

In conclusion, OpenTelemetry has been a game-changer for me in terms of observability. I've been able to gain valuable insights into my applications and services, identify issues, and optimize performance. If you're looking to improve the observability of your systems, I recommend giving OpenTelemetry a try. Some key takeaways from my experience with OpenTelemetry include:

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