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Observability vs Monitoring: What’s The Difference?

observability

researchHQ’s Key Takeaways:

  • Observability is the ability to infer the internal state of a system based on the system’s external outputs.
  • Monitoring is defined as the actions involved in observability: observing the quality of system performance over a time duration.
  • The observability of a system depends on its simplicity, the insightful representation of performance metrics and the capability of the monitoring tools in identifying the correct metrics.
  • Collaboration among cross-functional Devs, ITOps and QA personnel is critical in the successful design of a dependable system.

 

Enterprise IT and software-driven consumer product development are increasingly complex. The internet delivers IT infrastructure services from vast data centers at distant geographic locations. Companies consume these services as distributed functions like microservices and containers, across layers of infrastructure and platform services. Consumers expect rapid feature improvements through new releases via the internet.

To meet these end-user requirements, IT service providers and business organizations must streamline performance and improve stability and predictability of backend IT infrastructure operations—amid the inherent complexity of the IT systems. To do so, we closely observe and monitor metrics and datasets related to infrastructure performance in order to optimize system dependability.

These days, observability might seem like a buzzword; in fact, this traditional concept drives monitoring processes. Both the system observability and monitoring play critical roles in achieving system dependability—but they’re not the same thing. Let’s understand the differences between observability and monitoring, and how they are both critical to visibility and control in cloud-based enterprise IT operations.

What is observability?

Observability is the ability to infer internal states of a system based on the system’s external outputs. In control theory, observability is a mathematical dual (follows a direct conceptual mapping) to controllability, which is the ability to control internal states of a system by manipulating external inputs. In practice, however, controllability is difficult to evaluate mathematically; therefore, system observability is the method for evaluating outputs to reach meaningful conclusions about internal states of the system.

In enterprise IT, distributed infrastructure components operate through multiple abstraction layers of software and virtualization. This environment makes it impractical and challenging to analyze and compute system controllability.

Instead, common practice is to observe and monitor infrastructure performance logs and metrics to understand the performance of individual hardware components and systems. Advanced log analytics and AI (AIOps) evaluate incidents and events related to hardware performance in order to predict potential impact on system dependability. Then, your IT team can proactively adopt corrective measures to reduce the impact on end-users.

What is monitoring?

Observability is the ability to infer a system’s internal states. Monitoring, then, is defined as the actions involved in observability: observing the quality of system performance over a time duration. The monitoring action, which tools and processes support, can describe the performance, health, and relevant characteristics of a system’s internal states. In enterprise IT, monitoring refers specifically to the process of translating infrastructure log metrics data into meaningful and actionable insights.

A system’s observability property includes how well the infrastructure log metrics can infer the performance characteristics associated with infernal components. Monitoring tools analyze the infrastructure log metrics to deliver actions and insights.

Comparing observability and monitoring

Let’s use an example of a large, complex data center’s infrastructure system that’s monitored using log analysis and monitoring and ITSM tools. Analyzing too many data points continuously will generate volumes of unnecessary alerts, data, and false flags. The infrastructure may present low observability characteristics, unless the correct metrics are evaluated and the unnecessary noise is carefully filtered using AI-based infrastructure monitoring solutions.

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