A common beginner assumption is that once an application is deployed, the work is done. The professionals at a Parul University cloud session were clear that the opposite is true: deployment is where the real operational work begins, because now you have to see what your live system is actually doing. This visibility of the software—how it performs—is called observability. This stage closes the loop of the DevOps lifecycle.
Once software is live and serving real users, you cannot manage what you cannot see. The observability stage helps teams with visibility into how a system is performing, whether it is healthy, slow, or quick, and if something is going wrong, before customers can feel the impact. It is necessary because this will give teams insight, and they can work accordingly; they can make changes in the order of improving the software to make the experience for customers better; otherwise, they will be flying blind. Being able to find errors only when complaints are raised. With early-stage observations, they can easily fix the mistakes and find ways to run it efficiently.
Observability Vs Monitoring
These two terms are not identical, even though they might appear related. Monitoring is about seeing the known problems, keeping track of them, and tracking the specific conditions that might have led to the issue or conditions you already expect to matter (is the CPU too high, is the service up?).
Observability is defined as the ability to understand what is happening in the system from the data it has produced, including the problems you did not expect. The simple way to put it is that monitoring shows that something went wrong, while observability helps you find the reasons and understand why. Good operations need both.
The Three Pillars: Logs, Metrics, and Traces
Observability consists of three pillars, which are based on telemetry data, and a brief explanation is given below:
- Logs: Logs are like data entries or time-based records. It records individual events, which are used to understand the reason for what happened at a specific moment.
- Metrics: there are some measurements based on numbers; this includes memory use, request counts, or error rates. These are useful for finding trends and thresholds.
- Traces: seeing the path of a single request as it passes through a system, useful for finding where, exactly, something is slowing down or failing.
Through these pillars, a full picture can be gained where metrics can tell you something is off, logs tell you what happened, and traces tell you where. Today, many tools allow you to collect and analyse them at scale, with standards like OpenTelemetry gathering the data, and platforms such as Grafana, Dynatrace, and (on AWS) CloudWatch and OpenSearch help visualise and investigate it. For the AWS-specific implementation, see our guide to DevOps on AWS.
Alerts: Using Data To Turn Into Action
Data found through observability becomes useful when the right person receives or hears about a problem on time. Alerts are configured to trigger on specific conditions, such as a memory spike, overloading, an instant rise in failed requests, and route notifications through channels like Slack, PagerDuty, or even a direct phone call to say a server is down and needs immediate attention. The art lies in balancing, that if there are too few alerts, problems slip through, and if there are many, teams start ignoring them.
What an Anomaly Really Means- Anomaly Detection
At scale, systems can mark anomalies automatically, events that are not in line with normal patterns. Take, for example, an e-commerce application where transactions usually fall within the expected range; if an unexpected or unusual transaction comes in that is outside the range, the system works to mark it for attention and correction.
And an important point that often gets missed by beginners: anomaly discovery is not proof that something is wrong; it simply helps with showing that the event is unusual and needs action, to find out if it reflects genuine customer behaviour, a software bug, or something unexpected. Observability raises the question, and humans still answer it.
Observability Saves Money, Too
Observability is not only about catching failures. It also reveals waste. If monitoring consistently shows a system using only a small fraction of its allocated resources, that is a clear signal the organisation is over-provisioned and could scale down to save money. In this way, watching your systems closely protects both their reliability and your budget, a point that matters as much for large cloud deployments as it does for cost-sensitive student projects.
FAQs
What is observability?
Observability, put simple as the ability to understand what's actually happening inside a live software system, based on the data it puts out: metrics, logs, and traces. It's how you tell whether a system's healthy, catch something going wrong, and ideally spot problems before they ever reach users.
What is the difference between monitoring and observability?
Monitoring watches for known, expected problems (such as high CPU or a service being down), while observability is the broader ability to understand a system’s behaviour, including unexpected issues, from the data it produces. Monitoring tells you that something is wrong; observability helps you understand why. Teams need both.
What are the three pillars of observability?
The three pillars are logs (timestamped records of events), metrics (numerical measurements over time like CPU or error rates), and traces (the path of a single request through a system). Metrics show that something is off, logs show what happened, and traces show where.
What does it mean when a system flags an anomaly?
An anomaly is an event that falls outside normal patterns, such as an unusually large transaction. It is a flag for investigation, not proof that something is wrong. A human still needs to check whether it reflects genuine behaviour, a bug, or something else unexpected.
