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                     Introduction to ADF Pipeline 
Monitoring
 Monitoring pipelines in Azure Data 
Factory (ADF) ensures reliable data 
movement and transformation. It helps 
track execution status, detect failures, 
optimize performance, and maintain data 
quality across ETL and ELT workflows.
ADF Monitoring Hub Overview
 The Monitoring Hub in Azure Data 
Factory provides a centralized dashboard 
to view pipeline runs, activity runs, and 
trigger executions. It helps engineers 
quickly identify successes, failures, and 
running pipelines.
Monitoring Pipeline Runs
 Pipeline runs show overall execution 
status including start time, end time, 
duration, and result status (Succeeded, 
Failed, or Cancelled). This helps identify 
when and where failures occur.
Activity-Level Monitoring
 ADF provides detailed monitoring for 
each activity such as Copy Activity and 
Data Flow. It includes input/output 
details, execution duration, and error 
messages to isolate issues.
Trigger Monitoring
 Triggers automate pipeline execution. 
Monitoring triggers helps track execution 
history, identify missed or delayed runs, 
and validate scheduling and event-based 
triggers.
Alerts and Notifications
 ADF integrates with Azure Monitor to 
configure alerts. Notifications can be 
sent via email, SMS, or webhooks when 
pipelines fail or exceed execution 
thresholds.
Log Analytics Integration
 ADF logs can be sent to Azure Log 
Analytics for advanced querying using 
KQL. This enables trend analysis, failure 
detection, and creation of custom 
dashboards.
Debugging and Performance 
Optimization
 Monitoring helps debug issues by 
analyzing error messages and execution 
metrics. Engineers can optimize 
performance by tuning activities, 
parallelism, and compute resources.
Best Practices for ADF 
Monitoring
 Enable diagnostic logging, set alerts 
for critical pipelines, monitor activity-
level failures, and regularly review 
historical runs to ensure stable and 
scalable pipelines.
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