A company’s ability to innovate, best serve its customers, and remain competitive depends directly on the performance of the IT systems it relies on. Applications, infrastructures, and services must guarantee constant operability, high availability, and performance in line with the company’s needs.
When anomalies occur or when an application’s performance and scalability do not meet the expectations of a customer (even an internal one), it is essential to identify the cause promptly. This task is becoming increasingly difficult due to the growing complexity of modern systems. This is where monitoring and observability come into play.
IT Systems Monitoring: Fundamentals, Types, and Challenges
Monitoring has always been a fundamental practice in managing IT systems, developed to ensure the proper functioning of infrastructures, applications, and digital services.
Among the countless definitions, one provided by Splunk is particularly interesting, stating that monitoring consists of “collecting and analyzing predefined types of data (network bandwidth, CPU usage rates… editor’s note) to detect abnormal behaviors that could indicate potential problems.” In a world where a single minute of downtime can cost up to $9,000, having complete and real-time visibility on systems, applications, and data is not just an advantage but an absolute necessity. It is no coincidence that monitoring is the foundation of modern cybersecurity practices, IT operations, observability itself, and various IT Service Management practices.
What Can Be Monitored and Why
Every component of an IT system can be monitored. Over the years, this possibility has given rise to several specific disciplines and use cases, each focused on an aspect of IT operations.
Among the most common are:
- Network monitoring;
• Server monitoring;
• Security monitoring;
• Application Performance Monitoring (APM);
• API monitoring;
• Real User Monitoring (RUM) and Synthetic Monitoring.
To immediately understand the conceptual difference with observability, it is important to consider that monitoring is an action, specifically, the measurement of predefined metrics (CPU and memory usage, latency, availability, errors, data traffic, etc.), to assess the overall health of an IT system. In other words, it is a process by which IT observes specific parameters to detect anomalies and ensure the proper functioning of the ecosystem that supports the business and its processes.
IT Monitoring: The Challenges and Why We Need to Go Beyond
Monitoring remains a pillar of IT system management, but about a decade ago, it began to show its limits in the face of the growing complexity of modern architectures, which are increasingly characterized by the use of containers, microservices, pods, and orchestrators like Kubernetes.
Relying on traditional monitoring tools carries the risk of excessive fragmentation, as each part of the ecosystem requires specialized tools. This poses serious challenges in terms of integration and prevents the development of a unified view of the system, making it practically impossible to trace in real time the cause of anomalous behaviors, such as when users experience a generic “slowness” in the applications. If in the past the user experience was determined by one or a limited number of services (that could be monitored), today it results from the interactions among hundreds, sometimes thousands, of microservices—a situation that requires a different approach.
Moreover, the immense amount of data generated by modern systems exceeds the management capacity of traditional solutions, which often cannot process all this information with the necessary timeliness. This leads to essential data being overlooked and prevents the construction of a complete and contextualized picture, resulting in an overabundance of alerts and difficulty in separating relevant signals from those that can be disregarded.
These limitations have highlighted, over the years, the need for a more advanced, modern, and cloud-native approach to IT system management. Thus, observability has become fundamental.
Observability: The Key to Understanding and Managing Modern IT Systems
There is a conceptual difference between monitoring and observability. While monitoring is an action involving the collection and analysis of predefined metrics to assess the robustness and performance of a system, observability is an intrinsic property of the system itself. It must be designed to offer, via so-called telemetry data, continuous and deep visibility into its internal operations. As many commentators have pointed out, there is a significant difference in approach and mindset: monitoring is reactive, whereas observability is proactive, because the system itself outputs the necessary information to improve its management, possibly through data analysis techniques (AI).
An observable system proactively emits information about its state, offering its managers at least two major benefits:
- A holistic view of the system as a whole;
- An in-depth analysis of the causes of any anomalies, down to the level of an individual microservice.
This approach resolves some of the typical limitations of monitoring: if a service does not function as it should but does not impact the user experience, observability allows for a lower intervention priority; conversely, if the overall system is in difficulty, it enables the identification of the cause and immediate intervention.
One of the key differences between monitoring and observability is the depth of investigation. While monitoring focuses on the visibility of predefined elements of the system, observability provides all the data necessary to ask any question about the system’s behavior. However, although there is no specific tool that fully enables observability, monitoring tools (such as infrastructure monitoring, application monitoring, or synthetic monitoring) are essential components of any observability project, demonstrating the interconnection between the two paradigms.
How Observability Works and its Benefits
The informational foundation that enables the observability of IT systems is composed of three types of data: metrics, logs, and traces, which together provide complete visibility into the system’s behavior.
- Metrics are quantitative data that measure the behaviour and performance of the system, such as response times, CPU usage, and latency.
- Logs are files that record specific events within the system, such as errors, warnings, and human activities.
- Traces are extremely useful because they follow the flow of a single request through the various microservices, containers, and other infrastructures.
All these data are acquired from multiple sources and transformed into useful insights by advanced technologies such as AI and Machine Learning (ML), which enable the classification, organization, and especially the interpretation of data volumes that no human operator could ever manage manually.
Adopting observability allows for a deep understanding of complex systems, enabling IT teams to analyze how each component interacts within the architecture. This level of visibility is crucial for identifying the causes of any anomalies, reducing the MTTR (Mean Time to Recovery) and accelerating problem resolution.
Naturally, the ability to monitor in real time translates into optimized performance, improved scalability, and maximum uptime levels, which remain fundamental objectives regardless of the infrastructure’s complexity. The company can benefit from more satisfied customers and more productive employees, as systems operate smoothly and with performance aligned with their role within the organization.
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