Why Observability Is Harder Than People Think

Recently I had an SRE interview that forced me to confront something uncomfortable.

Halfway through the conversation I realized something very clearly:

This role is not for me.

So I told them directly.

“Sorry if I wasted your time.”

It wasn’t about embarrassment or failure. The interview simply exposed a reality about the field that many engineers underestimate.

Observability is much harder than people think.

And the reason is simple.

Observability isn’t about tools.

The Dashboard Illusion

Most teams believe they “have observability” because they installed the usual stack:

• Prometheus

• Grafana

• ELK / OpenSearch

• maybe tracing

There are dashboards everywhere.

CPU usage graphs.
Memory graphs.
Network graphs.
Request counts.
Latency charts.

It looks impressive.

But when something breaks in production, those dashboards often become surprisingly useless.

You stare at them.

The numbers move.

But the real question remains unanswered:

Why is the system failing?

That’s where the illusion breaks.

Dashboards are easy.

Understanding systems is hard.

Observability Is Actually About Questions

The deeper I looked into the SRE space, the more I realized something important:

Observability is not about collecting data.

It’s about being able to ask meaningful questions about a system.

For example:

• Why did latency spike for this specific user group?

• Which dependency introduced this failure?

• What changed between the last good deploy and now?

• Why is one service degrading while others remain healthy?

Answering these questions requires much more than graphs.

You need:

• instrumentation

• logs

• traces

• metrics

• system knowledge

• architectural understanding

And more importantly, you need the ability to connect those signals together.

Systems Are Messy

The real difficulty appears when systems grow.

Modern production environments look something like this:

• microservices

• containers

• load balancers

• message queues

• databases

• third-party APIs

• background jobs

• asynchronous events

Failures rarely happen in obvious places.

They emerge from interactions between components.

A small timeout in one service cascades into retries.
Retries overload another dependency.
Latency increases somewhere else.
Suddenly the system is failing.

Observability is the discipline of untangling those interactions.

Which is far more difficult than watching a CPU graph.

The SRE Mindset

That interview taught me something valuable.

SRE work requires a very specific way of thinking.

You need to constantly ask:

• What signals matter?

• What signals are missing?

• What failure modes exist?

• What assumptions could break?

It’s almost like debugging a distributed organism.

And that requires a level of systems thinking that goes far beyond installing monitoring tools.

Knowing When Something Isn’t Your Path

Walking away from that interview was strangely clarifying.

Not every technical path fits every engineer.

Some people thrive in pure systems reliability.

Others enjoy building products.

Others prefer infrastructure design.

What matters is understanding what kind of problems energize you.

That interview showed me something clearly:

Observability is one of the most intellectually demanding areas of engineering.

And the people who do it well deserve a lot more credit than they usually get.

Because when systems fail at scale, it’s the observability layer that determines whether you’re debugging reality…

or staring at graphs that tell you nothing.