The concept of observability represents the evolution of monitoring tools as we have known them so far. It is not so much a new trend that aims to replace IT monitoring tools, but rather an attitude that aims to make monitoring suitable for the new architectural environments of companies.
Borrowed from the mathematical theory of control, observability refers to the ability to deduce the internal states of a system based on external inputs. Applied to corporate IT, this approach modifies the classic monitoring understood as the process of translating the data from the infrastructure log metrics into meaningful insights. The property of observability includes the ability of the infrastructure log metrics to deduce the performance characteristics associated with internal components. Especially today, faced with distributed infrastructures that operate across multiple levels of software abstraction and virtualization, the need for performance analysis can no longer be limited to the reactive monitoring of individual components.
The complexity of the environments that drives observability
It should be noted that observability goes hand in hand with the increase in the complexity of the environments to be monitored, considering the increasingly high expectation from end-users. Such environments see the coexistence of cloud platforms, containers and microservices together with the growing adoption of DevOps practices aimed at improving the customer experience, increasing operational efficiency and accelerating the speed of development. In this scenario, Gartner argues that “traditional monitoring systems capture and examine signals in relative isolation, with alerts linked to threshold or rate-of-change violations. The observability tools, on the other hand, allow us to explain the unexpected behavior of the system more effectively ”. For this reason vendors such as Dynatrace, specialized in APM (Application Performance Monitoring) solutions, or Splunk, focused on SIEM (Security Information and Event Management) products, have made observability the key with which to enable companies to reduce both numbers of service outages both their impact and their severity.
Why system integrators help with observability
The suppliers mentioned above are not the only ones to have introduced a transformation of monitoring with a view to observability. Other brands such as Zabbix, Micro Focus and BMC, for example, are also moving in the same direction. This is the reason why it is not easy to carry out a software selection to identify the best tool to support observability. It is often from the combination of multiple systems that the organization can derive the greatest benefits for monitoring infrastructure and applications. In this sense, system integrators can become a valuable partner in choosing the observability model that adequately responds to the specific situation. Whether you are moving workloads to the cloud for the first time, or you are starting to deploy containerized applications or running microservices in production, it is essential to implement a monitoring method that - quoting Gartner again - to "identify and correct previously invisible anomalies".
Observability for less observable systems, too
The convenience of being supported by a system integrator with specific monitoring skills can be understood by thinking about what observability actually means. Observability depends on the simplicity of the system, the in-depth representation of performance metrics and the ability of the monitoring tools to identify the correct metrics. Unfortunately, this hypothesis collides with a complex reality: the continuous analysis of so many data generates volumes of alerts and false positives that are often unnecessary. In practice, when the infrastructure has characteristics of low observability, to be able to monitor it correctly it is essential to both evaluate the correct metrics and carefully filter the "noise", for example by using solutions based on Artificial Intelligence. That is why the concept of observability has now assumed a central role also in the development methodologies of the software life cycle, thanks to the preparation of systems that are natively able to provide the information needed to be observed.