Observability vs monitoring, what is the difference? Monitoring is the what to observability’s why. Here we dig into the differences.
Back in the old days of monolithic architecture, IT teams could rely on legacy monitoring tools to get visibility into apps, identify service-impacting incidents and make the necessary fixes. But as the industry moved to distributed serverless, cloud-native architectures, systems grew increasingly complex, layered and, as a result, fragile. Legacy monitoring tools were no longer sufficient.
As legacy monitoring tools became less effective, the importance of understanding a system’s operational state became more important. After all, most modern businesses rely on technology for their daily operations, and consumers continue to demand the latest and greatest technologies. Downtime simply isn’t tolerated. And if system failures do occur, they can be costly — to a company’s reputation, wallet and overall growth.
As a result of the shift from monolithic to complex, cloud-native environments, DevOps and SRE teams need automation-enabled tools that continuously examine data for quick incident detection, identification and mitigation. Enter observability.
The difference between observability and monitoring
Observability and monitoring have a symbiotic relationship, but each serves a different purpose. In short, monitoring is the what to observability’s why. Let’s dig into the differences.
Observability, which originated from Control Theory, is a property that indicates if a system generates enough meaningful data that human operators can understand its internal state based on its external outputs. Through instrumentation, IT and software systems provide insights, context and debugging from open telemetry data, including logs, metrics and traces. Observability tools find service-disrupting incidents within dynamic microservice environments, analyze the root cause and provide DevOps and SRE teams with mitigation strategies.
Monitoring is a subset of observability and indicates how well an observable system is performing. Unlike the granular insights, observability produces, monitoring provides a broad view of a system’s health and performance. Monitoring tools aggregate data, display a predefined collection of metrics and logs and detect known problems or occurrences like errors, traffic, latency and saturation. In other words, monitoring tells the IT teams when something is amiss, while observability provides insights into why there’s a failure.
While monitoring allows teams to track long-term trends and optimize system performance, it falls short when non-linear, unpredictable failures occur in distributed systems. This is where observability comes in. Observable systems are evidence-driven rather than rule- and model-driven and help IT teams rapidly fix incidents by providing actionable insights, even in complex microservice architectures that are constantly changing. By marrying both, IT teams get complete visibility across their systems, understand if the system is working and gain a real-time understanding of incidents.
The rise of observability in DevOps
Downtime is costly, so every business aims to increase uptime and decrease mean time to resolution (MTTR). Observability, particularly intelligent observability, gives system status updates throughout the software development lifecycle.
The DevOps community embraces observability as it notifies teams about an incident and then helps them troubleshoot the fix. Armed with incident notifications and actionable insights, DevOps engineers can work faster and smarter, accelerating response to service-affecting incidents and even pre-empting issues before they arise.
In addition, observability frees up teams’ time to pursue forward-thinking initiatives and helps minimize the disruption caused by delivering new technologies in complex production environments. This system reliability and innovation are invaluable to most modern businesses, especially as downtime equals big dollars and consumers continue to expect next-generation technologies.
Our modern, intertwined systems include dispersed microservices and containers and no longer follow straightforward, predictable rules. As a result, DevOps teams and SRE teams need advanced observability and monitoring tools.
Moogsoft’s complete intelligent observability platform automates the observability that DevOps and SRE teams need for continuous service assurance. With patented AI, the platform collects metrics and identifies anomalies in real time across the entire application stack, helping teams detect errors and avert incidents before they go into production. This situational awareness of digital services’ performance allows teams to operate less and innovate more.
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