Azure Data Factory is a powerful tool that allows you to perform various data integration and transformation tasks. However, it is important to monitor the different aspects of your data pipelines to ensure their successful execution and troubleshoot any issues that may arise. In this article, we will explore how to effectively monitor Azure Data Factory in SQL Server.

Monitor Pipeline in Debug Mode

When executing a pipeline under debug mode, the execution logs for each activity within the pipeline will be written to the Output tab in the pipeline Author page. This tab provides useful information such as the name and type of the executed activity, start time, execution duration, status, and more. You can also check the detailed graphical execution statistics for each activity, including data read and written, throughput, and copy time.

Monitor Manual Pipeline Run

When executing the pipeline under the manual trigger method, you can monitor the pipeline execution progress from the Pipeline Runs page under the Azure Data Factory Monitor window. This page displays the start time, end time, duration, execution method, and result of the pipeline execution. You can also check the resources consumed by the pipeline execution in the DIU unit.

Monitor Trigger Pipeline Run

If you associate a trigger to the Data Factory pipeline to schedule its execution automatically, you can monitor the pipeline execution result from the Trigger Runs of the Azure Data Factory Monitor window. This page shows the name and type of the executed trigger, trigger time, execution status, associated pipeline, and more. You can also get the same information from the Pipeline Runs page and filter the displayed result for a specific trigger type, trigger name, execution result, or time period.

Azure Data Factory Metrics

Azure Monitor is an Azure service that can be used to provide metrics and logs for most Azure services, including Azure Data Factory. The Azure Monitor metrics collect numerical data from the monitored resources at a regular interval, helping to describe the status and resource consumption of the monitored service. You can review the Azure Data Factory metrics by browsing the Monitor window and choosing the Alerts and Metrics page.

Azure Data Factory Alerts

Azure Data Factory allows you to define alerts based on the supported Data Factory metrics. These alerts can notify specific users when certain conditions are met. To create a new Data Factory alert, you can use the New Alert Rule button under the Alerts and Metrics page of the Monitor window. You can specify the name, severity, metric, pipeline, failure type, evaluation logic, and notification method for the alert.

Conclusion

Monitoring Azure Data Factory is crucial for ensuring the successful execution of your data pipelines. By monitoring the different aspects of your pipelines, you can identify and resolve any issues that may arise. Azure Data Factory provides various tools and features for monitoring, including debug mode, manual pipeline run monitoring, trigger pipeline run monitoring, metrics, and alerts. By utilizing these monitoring capabilities, you can optimize the performance and reliability of your data integration and transformation processes.