Acquire knowledge about the important aspects of data types in Azure Monitor

Azure Monitor handles several types of data to provide insights into resource performance, health, and usage. These data types are categorized primarily into metrics, logs, and related data streams. Here's an overview of the key data types in Azure Monitor:

Metrics

Definition:

Numerical data that measures resource performance and utilization over time.

Characteristics:

1. Lightweight: Designed for near real-time analysis.

2. Time Series: Data is stored with a timestamp, value, and metadata like resource and dimension.

3. High Frequency: Suitable for scenarios requiring frequent updates (e.g., CPU utilization every minute).

Examples:

1. CPU usage (%)

2. Memory usage (MB)

3. Network in/out (bytes)

4. Disk I/O (operations per second)

Use Cases:

1. Monitoring performance trends.

2. Setting thresholds for alerts.

3. Visualizing data in dashboards.

Logs

Definition:

Detailed, structured or unstructured data about operations and events.

Characteristics:

1. Rich and customizable.

2. Stored in a Log Analytics Workspace.

3. Queried using Kusto Query Language (KQL).

Examples:

1. Activity logs (control-plane operations).

2. Diagnostic logs (data-plane operations).

3. Application logs (custom events and traces).

4. Security logs (threat detections and audits).

Use Cases:

1. Root cause analysis.

2. Compliance auditing.

3. Querying large datasets for insights.

Traces

Definition:

Logs that track application code execution or custom events.

Characteristics:

1. Customizable by developers.

2. Includes information like stack traces, function calls, or custom events.

Examples:

1. Application Insights telemetry.

2. Custom events like user actions.

Use Cases:

1. Debugging application behavior.

2. Tracking user journeys through an application.

Events

Definition:

Discrete occurrences in the system that are tracked for auditing or monitoring.

Characteristics:

1. Often related to infrastructure or service changes.

2. Includes user-initiated and system-initiated events.

Examples:

1. Activity logs (e.g., resource creation).

2. Service Health events (e.g., maintenance or outages).

3. Resource Health events (e.g., degraded VM states).

Use Cases:

1. Monitoring administrative activities.

2. Tracking resource state changes.

Alerts

Definition:

Notifications or actions triggered based on specified conditions.

Characteristics:

1. Defined by users as rules on metrics or logs.

2. Configurable to invoke actions (e.g., send emails, execute webhooks).

Examples:

1. High CPU usage alert.

2. Failed deployment notification.

Use Cases:

1. Proactive monitoring.

2. Automating responses to critical issues.

Change Tracking Data

Definition:

Information about changes made to resources or configurations.

Characteristics:

1. Captures who made the change, when, and what was changed.

Examples:

1. Updates to a virtual machine configuration.

2. Changes to Azure Policy assignments.

Use Cases:

1. Audit and compliance.

2. Root cause analysis for configuration-related issues.

Dependency Data

Definition:

Data about relationships and dependencies between components.

Characteristics:

1. Captures external calls from applications (e.g., API calls, database queries).

Examples:

1. HTTP requests to external services.

2. SQL queries from a web application.

Use Cases:

1. Debugging application performance issues.

2. Identifying bottlenecks in dependencies.

Distributed Tracing Data

Definition:

End-to-end trace of a request as it flows through a distributed system.

Characteristics:

1. Tracks requests across multiple services or components.

2. Correlates telemetry data for performance analysis.

Examples:

1. Traces showing latency across microservices.

Use Cases:

1. Monitoring distributed applications.

2. Detecting and diagnosing latency issues.

Container Insights Data

Definition:

Metrics and logs specific to containerized environments like Kubernetes.

Characteristics:

1. Includes node, pod, and container-level metrics.

2. Stores logs like container logs and Kubernetes events.

Examples:

1. CPU/Memory usage per pod.

2. Container restarts.

Use Cases:

1. Monitoring Kubernetes clusters.

2. Optimizing container performance.

Network Monitoring Data

Definition:

Data about network performance and connectivity.

Characteristics:

1. Includes metrics and logs from services like Network Watcher.

Examples:

1. Packet loss.

2. Latency.

3. Network traffic flows.

Use Cases:

1. Diagnosing network issues.

2. Monitoring bandwidth usage.

Synthetic Monitoring Data

Definition:

Data from simulated user transactions or application tests.

Characteristics:

1. Generated by tools like Application Insights.

2. Includes availability tests and load testing results.

Examples:

1. Web application availability.

2. Response times from synthetic tests.

Use Cases:

1. Ensuring application availability.

2. Detecting performance degradation.

Insights Data

Definition:

Pre-aggregated data derived from specific Azure Monitor solutions.

Examples:

1. VM Insights: Metrics and logs about virtual machine performance and dependencies.

2. App Insights: Application telemetry data.

Use Cases:

1. Focused monitoring of applications or infrastructure.

2. Troubleshooting and performance optimization.

Key Differences Between Metrics and Logs

Best Practices for Using Data Types

1. Combine Metrics and Logs: Use metrics for real-time monitoring and logs for deep diagnostics.

2. Set Alerts: Configure alerts for critical metrics and log events to stay proactive.

3. Leverage Solutions: Use Insights (e.g., VM Insights, App Insights) to streamline data collection and analysis.

4. Export Data: Export logs and metrics to long-term storage or third-party tools for extended analysis.

Summary

By understanding and leveraging the different data types in Azure Monitor, you can build a robust monitoring strategy tailored to your application and infrastructure needs.