Monitoring at Different Levels in Azure Data Engineering

Building Azure data pipelines is only part of the story. The real challenge is making sure they keep running smoothly, stay secure, and cost-efficient. That’s where monitoring comes in — across every stage from ingestion to consumption.

Here’s how I approach monitoring in Azure data engineering, including how I implement custom monitoring, telemetry, and Log Analytics for deep visibility.

1. Ingestion Monitoring: Catch Issues Early

At the ingestion point, it’s critical to know if data arrives on time and complete. Azure Data Factory (ADF) and Synapse Pipelines have built-in monitoring dashboards showing pipeline run status, latency, and volume. But I always add custom telemetry — like tracking specific data quality metrics or retry counts — by pushing logs and metrics into Azure Log Analytics.

For example, I set up custom Azure Monitor alerts triggered by Log Analytics queries to notify me immediately if a pipeline is failing or taking longer than expected. This proactive approach helped me detect connectivity issues in patient data ingestion well before they impacted analytics.

2. Storage Monitoring: Protect & Track Your Data

Once data lands in Blob Storage or ADLS Gen2, I enable diagnostic logs and configure custom metrics to track operation counts, latency, and unauthorized access attempts. These logs stream into Log Analytics where I build queries to spot anomalies like unusual read/write spikes or access from unexpected IPs.

I also use Azure Policy to enforce encryption and secure access, then monitor compliance through telemetry dashboards, ensuring sensitive data is always protected.

3. Compute & Processing Monitoring: Optimize Jobs & Resources

For Databricks and Synapse Spark jobs, default monitoring shows job statuses and cluster health, but I take it further by integrating custom Spark application logs with Log Analytics.

This includes logging executor failures, shuffle metrics, and garbage collection times to gain fine-grained insights. I create dashboards with custom Kusto queries that help identify slow stages or resource bottlenecks.

For instance, spotting a pattern of executor failures led me to fix data skew in an ETL job, cutting runtime by 40%. Plus, I set up alerts for cluster auto-scaling events to keep costs optimized.

4. Data Quality Monitoring: Trust Your Data

Automated data validation is baked into pipelines using ADF Data Flows or PySpark scripts. But beyond pipeline errors, I send detailed validation results to Log Analytics, where I track null rates, duplicates, and schema changes over time.

This telemetry helps spot creeping data quality issues that don’t cause outright failures but can erode trust in analytics.

5. Security & Compliance Monitoring: Stay Protected

Security is a top priority, especially with sensitive data. I collect access logs from Azure SQL, Databricks, and storage, streaming them into Azure Sentinel for centralized threat detection.

Using custom telemetry and alerts, I monitor for unusual access patterns, role changes, and failed login attempts. This continuous monitoring ensures compliance with GDPR or other regulations and helps catch insider threats early.

6. Cost Monitoring: Keep Azure Spend in Check

Azure Cost Management gives a big picture of spend, but I complement it with custom telemetry that tracks cluster utilization vs active job time and storage growth trends.

By analyzing this data in Log Analytics, I identify underused clusters or orphaned resources, then apply automated scaling or shutdown policies to save costs without impacting performance.

7. Consumption Monitoring: Understand Data Usage

Finally, tracking how reports and dashboards are consumed helps balance performance and governance. Power BI usage metrics feed into telemetry dashboards, showing active users, query times, and access patterns.

I use this data to fine-tune row-level security and caching strategies, ensuring users get fast, secure access to the right data.

Bringing It All Together with Custom Telemetry & Log Analytics

What ties all these levels together is custom telemetry and centralized logging in Azure Log Analytics. Instead of relying solely on default dashboards, I instrument pipelines and jobs to emit rich logs and metrics tailored to my business needs.

With Kusto Query Language (KQL), I create powerful queries and alerts that catch subtle issues early — whether it’s a slow-running ETL step, a spike in data quality errors, or a suspicious security event.

This approach lets me maintain a holistic, real-time view of pipeline health, security, quality, and cost — enabling faster troubleshooting and smarter decision-making.