Introduction
The term observability comes from control theory, where it refers to the ability to understand the internal state of a system from its external outputs. In software engineering, observability means building systems that produce sufficient telemetry — metrics, logs, and traces — to understand their behavior without needing to deploy new instrumentation when something goes wrong. In 2025, observability has become a core requirement for operating reliable distributed systems. This article explains what modern observability looks like and how to achieve it.
The Three Pillars of Observability
Observability is commonly described through three pillars: metrics, logs, and traces. Metrics are numerical measurements of system behavior over time — request rates, error rates, latency percentiles, resource utilization. Logs are timestamped records of discrete events — errors, warnings, informational messages. Traces are records of requests as they flow through distributed systems, connecting the individual operations across multiple services into a coherent picture of a single transaction. Together, these three data types enable comprehensive understanding of system behavior.
OpenTelemetry: The Standard for Instrumentation
OpenTelemetry, or OTel, has emerged as the industry standard for instrumenting applications to produce telemetry data. It is a CNCF project that provides vendor-neutral SDKs for collecting metrics, logs, and traces in all major programming languages. By instrumenting your applications with OpenTelemetry, you can send telemetry to any compatible backend — Datadog, New Relic, Honeycomb, Grafana, and many others — without changing your application code. This portability is a major advantage over proprietary vendor SDKs.
Distributed Tracing
Distributed tracing is particularly valuable for microservices architectures where a single user request flows through multiple services. Without tracing, debugging a slow or failing request requires correlating log entries across multiple services, which is time-consuming and error-prone. With distributed tracing, you can see the complete path of a request, identify which service is slow or failing, and understand the causal relationships between events. Jaeger and Zipkin are popular open-source tracing backends. Vendor solutions like Datadog APM and Honeycomb provide richer analysis capabilities.
From Reactive to Proactive Observability
Traditional monitoring is reactive — you set thresholds and get alerted when metrics cross them. Modern observability enables a more proactive approach. Instead of asking whether CPU is above 80 percent, you ask questions like why this service suddenly has more latency than last Tuesday, or what changed in the last hour that correlates with the increase in error rates. Tools with strong query capabilities and anomaly detection enable these more sophisticated analytical workflows.
Service Level Objectives and Error Budgets
Service Level Objectives, or SLOs, are targets for the reliability of a service, expressed as a percentage of requests meeting defined performance criteria. SLOs are the basis for data-driven reliability engineering. When a service’s SLO compliance is tracked, teams can quantify their error budget — the allowable amount of unreliability. Error budgets make the trade-off between moving fast and maintaining reliability explicit and data-driven, replacing subjective arguments with objective measurements.
Conclusion
Observability is the foundation of reliable system operations. Organizations that invest in comprehensive instrumentation and observability tooling resolve incidents faster, prevent failures more often, and build more reliable systems over time. Our observability platform implementation and SRE services help organizations build and mature these practices. Visit our observability and monitoring engineering blog for more insights.
