Learn about the Azure Centralized Monitoring Architecture

Azure Centralized Monitoring is designed to collect telemetry data from diverse sources, process and analyze it on a scalable platform, and provide actionable insights to manage workloads efficiently. The architecture is divided into three core layers:

Data Sources

The Data Sources layer is where telemetry and logs are generated. It includes Azure services, third-party tools, infrastructure components, and applications. This layer captures all the raw signals needed for monitoring and analytics.

Data Source: Key Components and Details

1.1 Metrics

What are Metrics?

Time-series data that measures resource performance or utilization.

Examples:

1. Virtual Machine (VM) CPU usage percentage.

2. Disk read/write operations.

3. Application request response times.

Granularity and Retention:

1. Collected at short intervals (e.g., 1 minute).

2. Retained for 93 days by default in Azure Monitor.

1.2 Logs

What are Logs?

1. Event-driven, unstructured or semi-structured data.

2. Provides deeper insights into system operations and behaviors.

Types of Logs:

1. Activity Logs: Track operations on Azure resources (e.g., VM creation, deletion).

2. Resource Logs: Detail-specific activities within a resource (e.g., SQL query logs, API Gateway request logs).

3. Diagnostics Logs: Generated by Azure Monitor to track performance counters and errors.

4. Custom Logs: User-defined logs for applications or specific needs.

1.3 Application Insights Telemetry

Purpose:

Captures application-specific telemetry, such as:

• Exceptions, traces, and dependencies.

• User interaction events and performance counters.

Advanced Features:

1. Transaction Diagnostics: End-to-end tracking of user requests across microservices.

2. Dependency Tracking: Identifies performance bottlenecks in external dependencies (e.g., APIs, databases).

1.4 Traces

What are Traces?

A record of events occurring during an application's execution.

Purpose:

Helps debug distributed systems by tracing the flow of a request across components.

1.5 Change Tracking

Purpose:

Monitors configuration changes and updates in resources or environments.

Examples:

1. Detecting VM registry changes or software installations.

2. Capturing changes in Azure Security Groups or routing tables.

1.6 Third-Party and Hybrid Sources

1. Non-Azure Sources:

   • On-premises servers, VMs, and network devices.

   • Third-party services like AWS, Google Cloud, or private datacenters.

2. Tools:

   • Azure Arc: Extends monitoring to hybrid and multi-cloud environments.

   • Azure Monitor Agent: Unified data collection for hybrid environments.

Data Platform

The Data Platform layer processes, stores, and enriches data from the sources layer, transforming raw telemetry into actionable insights.

Data Platform: Key Functions and Components

2.1 Data Collection

1. Tools for Ingestion:

   • Azure Monitor Agent (AMA): Modern agent for collecting both metrics and logs.

   • Log Analytics Agent (deprecated): Legacy agent for telemetry collection.

   • Azure Diagnostics Extension: Captures VM-level performance counters and logs.

   • Application Insights SDKs: Captures in-app telemetry like custom events, dependencies, and exceptions.

   • Custom Data Collectors: For non-standard data ingestion via REST APIs or Event Hubs.

2. Integration with Other Tools:

   • Azure Data Factory for large-scale data ingestion.

   • Event Grid for real-time event-driven processing.

2.2 Data Storage

Purpose:

Scalable and secure storage for telemetry data.

1. Storage Options:

   • Log Analytics Workspace:

     • Centralized repository for logs, queries, and analytics.

     • Based on Azure Data Explorer, enabling high-performance querying.

   • Blob Storage:

     • Long-term storage for diagnostics and backups.

   • Azure Data Lake:

     • Optimized for big data analytics and machine learning workflows.

2. Retention:

   • Logs: Configurable up to 730 days in Log Analytics.

   • Metrics: Retained for 93 days, with export options for archival.

2.3 Data Enrichment and Processing

Purpose:

Extracts insights by processing raw telemetry data.

Key Tools:

1. Kusto Query Language (KQL):

   • Rich querying capabilities for logs.

   • Example: Identify VMs with disk utilization > 80% over the last 7 days.

2. Azure Functions:

   • Adds custom logic for real-time data transformation.

3. Azure Stream Analytics:

   • Real-time processing of streaming telemetry from IoT devices or Event Hubs.

Capabilities:

Correlation of multi-source logs (e.g., combining VM metrics with SQL diagnostics).

Anomaly detection using ML models.

2.4 Data Integration

Purpose:

Facilitates sharing of telemetry with external systems.

Tools:

1. Azure Event Hubs: Streams logs to third-party SIEM tools like Splunk or QRadar.

2. Logic Apps: Automates workflows, such as forwarding alerts to incident management tools.

3. Data Export Features:

   • Continuous export of logs to Blob Storage or Data Lake for compliance.

   • Metric export to Azure Monitor Metrics Explorer for analysis.

Consumption Layer

The Consumption Layer transforms processed data into actionable insights through visualizations, queries, alerts, and automated workflows.

Consumption Layer: Key Components and Features

3.1 Visualization and Dashboards

1. Azure Portal Dashboards:

   • Highly customizable dashboards that consolidate metrics, logs, and insights.

2. Azure Monitor Workbooks:

   • Interactive, query-driven dashboards.

   • Examples:

     • VM performance and dependency visualization.

     • Application transaction and user interaction analysis.

3. Power BI Integration:

   • Advanced reporting with data visualizations and trends.

3.2 Log Query and Analysis

1. Log Analytics Workbench:

   • Central interface for querying telemetry data.

   • Uses KQL for:

     • Trend analysis.

     • Root cause investigations (e.g., application crashes, dependency failures).

2. Application Insights Analytics:

   • Optimized for application telemetry.

   • Distributed tracing for debugging microservices.

3.3 Alerts and Notifications

1. Azure Alerts:

   • Proactive notifications based on metrics or log thresholds.

   • Supports dynamic thresholds, learning historical patterns to reduce noise.

   • Example:

     • Alert if average VM CPU utilization exceeds 75% for 10 minutes.

2. Notification Channels:

   • Email, SMS, ITSM tools (e.g., ServiceNow), or custom webhooks.

   • Supports automation tools like PagerDuty or Slack integrations.

3.4 Automation

1. Azure Logic Apps and Automation Runbooks:

   • Automates incident response (e.g., restarting a failed VM).

   • Integrates with DevOps pipelines for CI/CD telemetry.

2. Azure Functions:

   • Triggers custom workflows (e.g., scale out resources on demand).

3.5 Insights and Recommendations

1. Azure Monitor Insights: Pre-built solutions for resource-specific monitoring:

   • VM Insights: Resource usage and dependency mapping.

   • Container Insights: Kubernetes health and pod performance.

   • SQL Insights: Query performance and database-level analytics.

2. Azure Advisor: Offers recommendations based on telemetry for cost optimization, performance improvement, and security enhancements.

End-to-End Workflow

1. Data Collection: Azure resources emit metrics and logs via Azure Monitor Agents. Telemetry is ingested into Log Analytics Workspaces.

2. Processing and Storage: Data is enriched using KQL queries and processed with Stream Analytics for real-time scenarios. Logs are stored in scalable repositories like Log Analytics and Azure Data Lake.

3. Visualization and Alerts: Dashboards, Workbooks, and Power BI provide insights. Alerts and automation workflows trigger corrective actions.

4. Integration and Optimization: Export data to third-party systems. Use Azure Advisor and Insights for recommendations.

Use Cases

1. IT Operations: Monitor VM health, storage performance, and network traffic. Automate scaling based on workload trends.

2. Application Monitoring: End-to-end visibility into microservice architectures. Diagnose slow API responses or database issues.

3. Security and Compliance: Centralized log management for audit trails. Real-time alerts for unauthorized access or resource changes.

4. Hybrid Environments: Unified monitoring for on-premises and cloud workloads using Azure Arc.

Summary

This detailed version ensures a thorough understanding of Azure Centralized Monitoring and how its layers interact to deliver a robust monitoring ecosystem. Let me know if you’d like further breakdowns, diagrams, or examples!