Monitoring Told You It’s Broken. Great. Now What?

Hey there,

Your monitoring dashboard lights up red. The alerts are firing. You know something is broken.

But you have no idea why, where, or, most importantly, how to fix it.

That gap between “it’s broken” and “here’s why” is where observability lives.

In today's issue, we explore:

• What observability actually means for your applications

• The building blocks that make observability work

• Real-world examples where observability prevents disasters

Let’s dive in.

Observability is the practice of instrumenting your applications to measure internal runtime states and behaviors. 

While monitoring tells you about surface-level events, like a CPU spike or slow web page loads, observability enables you to determine the root cause. It traces the complete journey of a failed transaction across every component in your application.

By capturing detailed metrics, logs, and traces, you gain an inside-out understanding of systems that lets you proactively improve user experiences, block potential downtime, and rapidly debug issues as your services scale up.

Modern applications are no longer monoliths. A single user action can spin hundreds of interconnected microservices. Without observability, you are constantly managing unknown issues that emerge unpredictably.

Take the classic scenario: A major e-commerce platform during its biggest sale day. Checkouts suddenly fail at the final confirmation. Without observability, engineers scramble blindly.

With observability in place, actively deployed, those engineers can quickly pinpoint the exact breakdown:

• Granular traces show the API call from the checkout service to the payment gateway is timing out due to excessive load on a backend database.

• Metrics confirm the database cluster is overloaded.

• Logs provide specific error messages for those failed payment transactions.

This precise data allows the team to scale capacity and restore service swiftly. Customers might notice brief slowness, but a catastrophic outage is avoided.

Want the complete breakdown of observability pillars and implementation strategies? Check out our comprehensive guide here.

Observability is built on four main pillars:

Metrics: Quantitative time-series data such as request speeds, error rates, and resource usage. Metrics help set performance goals and detect deviations from normal operation.

Logs: Records of discrete events that provide an audit trail and crucial clues for troubleshooting when viewed alongside traces and metrics.

Traces: Maps the path of individual requests across distributed services. In highly distributed architectures, traces reveal how individual components affect the final user experience.

Profiling: In-depth insights into resource consumption, particularly CPU and memory usage, pinpointing performance bottlenecks that simple metrics often miss.

No single pillar gives you the complete picture. Together, they establish integrated observability into complex modern applications.

Observability delivers significant business value across many sectors:

In fintech, traces help fraud teams optimize detection models, metrics identify latency issues in trade execution, and logs improve conversion funnels. 

For e-commerce, traces enable quick experimentation, metrics quantify software impact on business KPIs, and logs reveal checkout abandonment points that can guide placement and personalization improvements. 

In gaming, metrics on session length optimize engagement, traces diagnose lag under new releases, and logs inform economy balancing.

Engineering teams are increasingly moving toward unified observability platforms that merge metrics, logs, and traces into a single source of truth, enabling them to not only detect issues but also fully understand their root cause and system-wide impact.

Without properly aggregating and analyzing observability data, signals just become noise.

Strengthen your understanding of modern observability with these focused, practical resources.

Fixing Observability — A Call to Rebuild It Together: A look at what’s broken in observability today and what must change across tools, teams, and culture.

Observability Is Stuck in the Past. Your Users Aren’t: How traditional monitoring falls short in distributed systems and what a modern approach requires.

How can you handle observability in a cloud-native application?: Methods and patterns for visibility across microservices, containers, and dynamic environments.

10 Observability Best Practices Every DevOps Should Implement: Core practices that improve reliability, cut noise, and speed up response in complex systems.

Ready to dive deeper into implementation best practices, specific tooling options, and how observability powers DevOps and AIOps? Read the complete guide here.

And it’s a wrap!

See you Friday for the week’s news, upcoming events, and opportunities.

If you found this helpful, share this link with a colleague or fellow DevOps engineer.

Divine Odazie
Founder of EverythingDevOps