📊 Monitoring & Observability
This section describes how we monitor integration components and data flows in Azure to ensure reliability, traceability, and proactive incident management.
🎯 Objectives
- ✅ Detect issues early and reduce downtime
- 🔍 Enable root cause analysis and traceability
- 📈 Monitor performance and usage trends
- 🔔 Alert relevant teams in case of failures or anomalies
🧰 Azure Monitoring Tools
We leverage native Azure services to implement observability across our integration landscape:
🔎 Azure Monitor
- Centralized platform for collecting metrics, logs, and diagnostics.
- Used to monitor Azure Data Factory, Function Apps, Storage Accounts, and more.
📄 Log Analytics
- Aggregates logs from multiple sources into a single workspace.
- Enables querying and correlation of events across services.
📡 Application Insights
- Used for monitoring Function Apps and custom applications.
- Tracks requests, dependencies, exceptions, and performance metrics.
📬 Azure Alerts
- Configured to notify teams via email, Teams, or ITSM tools.
- Triggered based on thresholds, failures, or custom queries (KQL).
🧩 What We Monitor
| Component | What We Track |
|---|---|
| Data Factory | Pipeline runs, activity failures, trigger status |
| Function Apps | Execution time, exceptions, dependency failures |
| Data Lake | Access logs, file ingestion, permission changes |
| Key Vault | Secret access, expiration, unauthorized attempts |
| Linked Services | Connection failures, authentication issues |
🛠️ Best Practices
- Enable diagnostic settings on all critical Azure resources.
- Use resource tags to group logs by project, environment, or owner.
- Define naming conventions for alerts and dashboards.
- Store logs for a minimum of 30 days (or more based on compliance needs).
- Regularly review alert noise and fine-tune thresholds.
📋 Example: Monitoring a Pipeline Failure
- A pipeline in ADF fails due to a missing file.
- The failure is logged in Azure Monitor and sent to Log Analytics.
- An alert is triggered and sent to the integration team via Teams.
- The team uses KQL to investigate the root cause and fix the issue.
🧠 Monitoring is not just about detecting problems — it's about building confidence in our systems and enabling continuous improvement.