Hawatel

There are only two ways to discover system dependencies:

👉 Map them intentionally
👉 Discover them during a failure

One costs engineering time. The other costs money, and reputation.

Prove us wrong.

Enterprise monitoring often works like this:

- Network team → their own tools
- Infrastructure → separate Linux & Windows monitoring
- Databases → isolated systems
- Applications → APM (or nothing)
- Logs → somewhere on servers

Everyone sees their piece. No one sees the whole system.

It’s like lighting a room with flashlights 🔦 Each one works perfectly.

But the room is still dark.

Let’s take a look at your monitoring setup: https://hawatel.com/en/blog/why-do-enterprises-have-it-infrastructure-monitoring-but-still-lack-operational-visibility/

The real risk isn’t components. It’s dependencies.

Typical chain: App → API → services → databases → storage → network

When something degrades:

- nothing “breaks” immediately
- latency increases
- timeouts grow
- users notice before alerts fire

Without dependency mapping, you can’t answer: “If this fails, what else breaks?”

And that’s the only question that actually matters.

Sounds familiar? Then let's have a talk:
👉 https://hawatel.com/en/blog/why-do-enterprises-have-it-infrastructure-monitoring-but-still-lack-operational-visibility/

"75% of IT teams in UK have experienced an outage caused by a missed alert in 2025,” ITPro.com recently reported.

And that’s the moment when it’s worth asking an uncomfortable question:

Is the real problem actually a lack of monitoring ❓️

In most enterprise environments, there’s already more data than anyone can realistically handle.

The problem becomes:

📉 noise instead of signal
🔗 lack of correlation
👤 unclear ownership
🔔 dozens of tools firing alerts independently of each other

As a result, teams stop responding proactively. Not because they don’t have alerts, but because they stop trusting them.

That’s what operational blindness looks like.

How do you deal with alert fatigue in your environment?

Siloses don’t happen by accident.

They’re a natural result of how enterprise IT works:

Teams buy tools “for now”
Different domains → different technologies
Budgets are split, not centralized
Integrations are always “later”

And the truth is, that the bigger the infrastructure, the worse the visibility

More tools ≠ better control
More tools = more fragmentation

👉 Read more on our blog: https://hawatel.com/en/blog/why-do-enterprises-have-it-infrastructure-monitoring-but-still-lack-operational-visibility/

Tuesday, 10:30. Production issue. Users report slow transactions (8–10s instead of 1s).

App team checks → everything looks fine
CPU OK, processes OK → “not us”

Meanwhile:

- Core router buffers at 98%
- Redundant path partially down
- Network degradation — but no alert

👉 Correlation happens after 2 hours
👉 Incident lasts the entire time

Not because of missing data, because no one saw the full chain.

If this sounds familiar, read the full breakdown on our blog: https://hawatel.com/en/blog/why-do-enterprises-have-it-infrastructure-monitoring-but-still-lack-operational-visibility/

Your company has IT monitoring, so why you don’t have visibility?

Most enterprise environments look “under control” — multiple monitoring tools, dashboards, alerts, until something breaks.

Then suddenly:
- there’s a lot of data
- but no clear answers

That’s the difference no one talks about:

👉 Monitoring ≠ operational visibility

You don’t need more data. You need a way to understand what it actually means.

Full breakdown on our blog: https://hawatel.com/en/blog/why-do-enterprises-have-it-infrastructure-monitoring-but-still-lack-operational-visibility/

🚨 09:14 — synthetic alert: login form is down.

Most teams start searching at this point. 🔍 Checking servers, asking colleagues, opening yet another dashboard.

In a well-designed environment — the answer is already on the screen.

Here's what that looks like in practice:
Layer 1️⃣ — synthetic monitoring catches the problem first. Every minute it walks the user's path and signals that something stopped working from the end-user perspective. Before anyone calls.
Layer 2️⃣ — Elastic APM shows what happens after the click. Distributed tracing from the request through microservices all the way to the database. In this case: a session database query taking 8 seconds.
Layer 3️⃣ — Zabbix confirms why. I/O wait on the database server. The problem isn't in the application code — it's in the resources.
Layer 4️⃣ — Grafana ties it all together in a single view. One dashboard, one timeline, full correlation.

Result: RCA in 3 minutes, not 3 hours.

That's the difference between tools and architecture. Each of these tools alone gives you a fragment of the picture. Together — they deliver full operational visibility.

Synthetic monitoring isn't a standalone island. It's the first layer of a system that works as a whole.

Imagine finding out about an application issue from a customer. 📞

Now imagine finding out an hour earlier — before the customer even reaches the page.

That's the difference between real user monitoring (RUM) and synthetic monitoring.

Real User Monitoring collects data from real users. It shows what they actually experienced — how long they waited, where they dropped off, what didn't work. Invaluable. But reactive by nature: to collect data, something has to happen first. Someone has to hit the problem. 👤

Synthetic monitoring doesn't wait. It opens the page, clicks, fills out the form, and checks — every minute, around the clock, at zero production traffic. It detects degradation in the middle of the night, before the first user shows up. 🤖

One looks backward. The other looks forward. ⚖️

The optimal environment uses both — RUM tells you what customers experienced, synthetic tells you what's waiting for them.

🚨 The question isn't: which one to choose? It's: can you afford to be missing either?

Your monitoring checks if the server is alive. But who checks if the user can actually log in? 👤

That's exactly the gap synthetic monitoring fills.

It doesn't wait for production traffic. It doesn't wait for a customer complaint.
Every so often, every minute it can open the page, fill out the form, click the button, and verify it got the right response back. 🔁

In practice, it works across three layers:
👉 Simulated transactions — scripts mirroring real user flows: login, search, order completion. Every step verified for response correctness, not just availability.
👉 HTTP/HTTPS checks — cyclic endpoint verification. The simplest layer, but already catches certificate errors, redirects, timeouts, and downtime.
👉 Web scenarios — multi-step tests of complete business flows: from opening the page, through filling out the form, to confirming the operation.

The difference from classic infrastructure monitoring is fundamental: instead of asking the server "are you alive?" — synthetic monitoring walks the user's path itself and checks whether it reached the destination.

It runs in the middle of the night. 🌙 At zero traffic. Before anyone hits the problem.

Does your monitoring check the server or your customers?