Monitoring in Azure with Azure Monitor

You cannot operate what you cannot see

Migrating to Azure without monitoring is like driving with the car's dashboard off: everything seems fine until it suddenly stops. Observability is the ability to understand the environment's internal state from the signals it emits — and in Azure the central platform for this is Azure Monitor. It brings together metrics, logs, and traces from the whole stack, from infrastructure to application, in one place.

As a Microsoft partner and CSP, RHC deploys observability so problems are detected before they become incidents and so cost and performance stay under control.

The three signals of observability

Modern observability rests on three signal types:

• Metrics — numerical values over time (CPU, memory, latency, requests). Lightweight and ideal for trends and alerts.
• Logs — detailed records of events, essential for root-cause investigation.
• Traces — the path of a request crossing several services, revealing where the bottleneck is.

Azure Monitor collects all three and makes them available for query, visualization, and alerting.

The Azure Monitor components

Log Analytics and KQL

The heart of log analysis is the Log Analytics Workspace, where data is stored and queried with the Kusto Query Language (KQL) — a powerful, readable language to filter, aggregate, and correlate events. Centralizing logs from multiple workloads in one workspace lets you correlate signals that, in isolation, would make no sense.

Application Insights

While infrastructure metrics show the machine's health, Application Insights shows the application's health: response time, failure rate, exceptions, slow dependencies, and the map of how components communicate. It is essential to diagnose why an application is slow even when the infrastructure looks healthy.

Alerts that matter

Collecting data without acting on it is waste. Azure Monitor alerts fire when a condition is met — CPU above a threshold, elevated error rate, budget overrun. Best practices avoid alert fatigue:

1. Alert on symptoms, not noise — focus on what affects the user.
2. Define clear severities to prioritize response.
3. Use dynamic thresholds that learn the normal pattern, reducing false positives.
4. Connect to actions — notify teams, open tickets, or trigger remediation automation.
5. Review periodically, retiring alerts that only generate noise.

Beyond health: cost and security

Azure Monitor is not just for performance:

• Cost — integrated with Cost Management, it helps detect anomalous consumption before it becomes a billing surprise.
• Security — logs feed Microsoft Sentinel (SIEM) for threat detection and Defender for Cloud for security posture.
• Capacity — usage trends anticipate the need to scale or right-size.

Building observability from scratch

A pragmatic path to deploy:

1. Enable platform metrics — most come by default, at no extra cost.
2. Create a central Log Analytics Workspace and route relevant logs.
3. Instrument applications with Application Insights.
4. Build dashboards by audience — operations, management, business.
5. Configure essential alerts on user symptoms.
6. Control log retention to balance visibility and cost.

Watch the ingestion and retention cost: excessive logs generate expense. The discipline is to collect what creates value, with appropriate retention per data type.

Checklist / Key takeaways

• Observability rests on metrics, logs, and traces.
• Centralize logs in a Log Analytics Workspace and query with KQL.
• Use Application Insights to see application health.
• Configure alerts on symptoms and avoid alert fatigue.
• Connect monitoring to cost and security (Sentinel, Defender).
• Control log retention to balance visibility and cost.

Well-deployed observability turns operations from reactive to proactive: you see the problem coming and act before the user feels it. RHC builds this layer aligned with the client's workloads and budget.