As more and more IT organizations pivot from strategies rooted in monitoring to ones that aim to achieve observability, one of the first questions that teams need to answer is, “What is the difference between the two?”
That’s a somewhat loaded question because there are some differences of opinion out there regarding what, exactly, distinguishes monitoring from observability. But in general, the differences can be distilled into two main points, as we will explain.
The first step in understanding the difference is to understand what each term means on its own.
The classic definition of observability is that it is the use of external data outputs to understand the internal state of a system. In the IT industry specifically, observability could be defined as the use of logs, metrics and distributed traces to understand the state of a complex software system.
Meanwhile, monitoring is the process by which data is collected from a system. In IT, monitoring is how teams collect logs, metrics and traces and move them to a place where they can be analyzed.
There are some obvious similarities between the two. Both involve (at least in part) the collection of diverse sets of data. Both also serve the goal of helping teams identify problems within their software stacks and deliver a high-quality user experience.
But the similarities end there. Ultimately, monitoring and observability are distinct concepts and processes, with two main differences that distinguish them from each other.
The first is that observability focuses on interpreting and understanding data, whereas monitoring is merely the collection of data.
In this sense, you can think of monitoring as one of the processes that makes observability possible. But observability requires more than monitoring. In addition to collecting data, observability also involves the correlation of data from multiple sources and the identification of relevant patterns or anomalies within that data.
The second main difference is that monitoring simply alerts you when something goes wrong. Observability goes further by helping you understand why it’s wrong and how to fix it.
For example, a monitoring tool might tell you that your application’s response rate is no longer adequate. But you need observability to figure out which specific microservices within the application are causing the problem. Using that information, you can plan and execute an effective response to reliability issues using an incident management platform.
With monitoring alone, you only know that something is broken, not how to fix it.
Although it’s common to speak in terms of “observability versus monitoring,” the reality is that you can’t have observability without monitoring. (You could monitor without observing, but doing so would significantly reduce your ability to understand and resolve problems quickly.)
So, don’t think of it as an either/or proposition. Think of them as concepts and practices that go hand-in-hand, even if they perform different functions.
Redis is taking it in the chops, as both maintainers and customers move to the Valkey Redis fork.
GitLab Duo Chat is a natural language interface which helps generate code, create tests and access code summarizations.
Expect attacks on the open source software supply chain to accelerate, with attackers automating attacks in common open source software…
The emergence of low/no-code platforms is challenging traditional notions of coding expertise. Gone are the days when coding was an…
Datadog today published a State of DevSecOps report that finds 90% of Java services running in a production environment are…
Linux dodged a bullet. If the XZ exploit had gone undiscovered for only a few more weeks, millions of Linux…