When building and running software, knowing what’s happening inside your system is just as important as writing good code. That’s where observability in DevOps comes in. It helps teams understand what’s going wrong, where the issue is coming from, and how to fix it before it causes bigger problems.
As software systems grow more complex, especially with the shift to cloud-native apps and microservices, teams face more unknown issues that can’t be solved with basic monitoring alone. Observability gives teams the ability to look deeper into how their applications behave.
Instead of just knowing something broke, teams can understand why it broke and what parts of the system were involved. This makes it easier to respond to incidents, improve performance, and deliver updates without constantly putting out fires.
In this blog, we’ll explain what observability means, what tools help support it, and how real companies use it every day to keep their systems running smoothly.
What Is Observability in DevOps?
In simple terms, observability is about understanding the reason behind your system’s behavior. Instead of just knowing something went wrong, observability helps you find out exactly where and why it happened, using data like logs, metrics, and traces.
This approach is helpful when dealing with complex systems that involve multiple services, APIs, or third-party tools. Without observability, teams are often left spending hours trying to track down the root cause of issues.
By bringing together data from across your applications and infrastructure, observability gives you a picture of how everything is working, both when things go right and when they don’t.
The Three Key Parts of Observability
Observability is based on three main types of data: logs, metrics, and traces. These work together to give you a full idea of your system.
Logging
Logs are written records of what your system is doing. They show things like errors, warnings, or events happening in real time. Logs help developers debug and track down where things went wrong.
Metrics
Metrics are numbers that show how your system is performing. These include data like server load, response times, or the number of active users. Metrics help you spot trends and find out when something isn’t performing as it should.
Distributed Tracing
Distributed tracing shows how a request moves through different services in your system. If your app is built with microservices, tracing can help you follow a user request from start to finish and see where delays happen.
Monitoring vs. Observability: What’s the Difference?
While they’re often used together, monitoring and observability are not the same.
Monitoring involves collecting predefined data points (like CPU usage or uptime) to detect known issues.
Observability is more dynamic. It enables teams to ask new questions, explore unknown issues, and gain deeper context, even if the system is failing in unexpected ways.
What are the Observability Tools in DevOps Pipelines?
To make observability work in DevOps environments, teams use a set of tools that help collect, analyze, and visualize data from different parts of the system. These tools are often added directly into CI/CD pipelines so that every stage, from development to production, is monitored and understood in real time.
Here are some of the most widely used observability tools in DevOps pipelines and what they do:
Prometheus
Prometheus is a powerful tool that collects and stores data, mostly metrics. It’s often used to monitor things like CPU usage, memory, request rates, and error rates. It allows teams to set up custom alerts when performance drops or when something unusual happens. Prometheus is especially popular because it works well in dynamic environments like Kubernetes.
Grafana
Grafana is a visualization tool that helps turn raw metrics into useful dashboards. It works with Prometheus and other data sources to create graphs, charts, and alerts. DevOps teams use Grafana to monitor application health, server performance, and infrastructure activity clearly and visually.
ELK
The ELK Stack is a set of three tools, Elasticsearch, Logstash, and Kibana, that work together for log management:
Logstash collects and processes logs from different sources.
Elasticsearch stores and indexes those logs so they can be searched quickly.
Kibana allows you to explore and visualize the log data through dashboards.
This stack is useful when teams need to troubleshoot issues by digging deep into error logs or tracking events over time.
Jaeger and Zipkin
Both Jaeger and Zipkin are open-source tools used for distributed tracing. They help DevOps teams understand how a request moves across different services in a microservices setup. These services often run in containers using tools like Docker, which makes it easier to manage and scale them in production.
OpenTelemetry
OpenTelemetry is a modern framework that standardizes how applications generate and send observability data. It supports logs, metrics, and traces. It's vendor-neutral, which means you can use it with a variety of backends like Prometheus, Jaeger, or commercial platforms. Many teams use OpenTelemetry to simplify data collection across their entire stack.
Use Cases of Observability in DevOps
Here’s how well-known companies use observability to solve real problems:
Netflix: Fast Incident Response at Scale
Netflix runs a huge number of services to stream videos across the world. They use observability to track everything in real time, helping them detect and fix issues quickly. According to their engineering team, they’ve built internal tools to automate incident analysis, which helps them respond fast, even during peak traffic times like big show releases.
Canva: End-to-End Tracing for Better Observability
Canva uses observability to monitor its microservices and ensure reliable performance during frequent deployments. It implements end-to-end tracing with OpenTelemetry and Honeycomb, so their engineering team can track requests across services and debug issues quickly.
Uber: Troubleshooting Complex Microservices
Uber has many different services working together: maps, ride requests, payments, and more. They use tools like Prometheus and distributed tracing to monitor how each request moves through their system. Uber’s engineering blog explains how they use Jaeger to trace requests across microservices and quickly find where things go wrong when a ride gets delayed or a payment fails.
Conclusion
Observability helps teams get to the root of problems without guesswork. Instead of just reacting when something breaks, it allows you to understand what happened, where it started, and how to fix it using the correct data.
Teams that include observability in their DevOps pipeline can detect issues earlier, fix them faster, and improve how their systems perform over time. It reduces the pressure during incidents and makes troubleshooting a lot easier.
Even as systems grow with more services and tools, observability keeps everything connected and clear. It gives teams better control and helps them deliver stable, reliable products that users can count on. Observability becomes even more valuable in setups using Docker-based microservices, helping teams stay ahead of issues.
Think faster, fix smarter, and build with observability at the core.
The examples of Netflix and Uber really hit home. We started integrating OpenTelemetry with Prometheus in our CI/CD pipeline, and it’s been a game-changer for catching issues before users notice. I’d love to see more on how teams handle alert fatigue while maintaining observability.
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