Data Factory Monitoring

When a Data Factory is associated with Serverless360 Composite Application, it is possible to monitor Data Factory in various perspectives using the monitors in Serverless360.

Monitoring Azure Data Factory State

If Azure Data Factory is one of the key resources in your Business, if it's state(with all the Properties having values as expected) meeting the expectation is critical. Use Status Monitor in Serverless360 to get alert via any of the Notification channels available when the resource is not in the expected state.

Monitoring your Azure Data Factory also includes

Trigger Monitoring

Acheive Monitoring on Data Factory Triggers

Enable Status monitor for Data Factory Triggeres and check whether the current state is like the expected state, if not get notified.
Monitor Triggers

Auto Correct Azure Data Factory Triggers State

Enable Threshold Monitor and be rest assured that your Data Factory Trigger is always in the expected state.

Monitoring Azure Data Factory based on Metrics

If the intension is to monitor and get notified when ever specific values or data in Data Factory is breached then you can acheive it through Serverless360's Data Monitor.

Monitoring Purpose	Metrics to Choose
Cancelled Runs	Cancelled activity runs, Cancelled pipeline runs metrics, Cancelled trigger runs metrics
Failed Runs	Failed activity runs, Failed pipeline runs, Failed trigger runs
Succeeded Runs	Succeeded activity runs ,Succeeded pipeline runs metrics, Succeeded trigger runs
Integration runtime	Integration runtime available memory (Bytes), ntegration runtime available node count (Count), Integration runtime CPU utilization (%), Integration runtime queue duration (Seconds), Integration runtime queue length (Count)
Factory Size	Maximum allowed factory size (GB unit), Total factory size (GB unit)
Entities Count	Maximum allowed entities count, Total entities count

Monitoring Purpose

Metrics to Choose

Cancelled Runs

Cancelled activity runs, Cancelled pipeline runs metrics, Cancelled trigger runs metrics

Failed Runs

Failed activity runs, Failed pipeline runs, Failed trigger runs

Succeeded Runs

Succeeded activity runs ,Succeeded pipeline runs metrics, Succeeded trigger runs

Integration runtime

Integration runtime available memory (Bytes), ntegration runtime available node count (Count), Integration runtime CPU utilization (%), Integration runtime queue duration (Seconds), Integration runtime queue length (Count)

Factory Size

Maximum allowed factory size (GB unit), Total factory size (GB unit)

Entities Count

Maximum allowed entities count, Total entities count

You can even perform cross comparion on these metrics for better efficiency.
cross comparision

Get Historical reports of performance of Data Factory using the DataMonitor Dashboard.

If the intention of the user is to get the total number of Succeeded pipeline runs or any run details in the past, they can use DataMonitor Dashboard.
Historic Data

We'd love to hear your thoughts

Please visit our feedback system to suggest new Features or Enhancements. You can also take a look at our Roadmap

Data Factory Monitoring