Visually monitor Azure Data Factory

APPLIES TO: Azure Data Factory Azure Synapse Analytics

Once you've created and published a pipeline in Azure Data Factory, you can associate it with a trigger or manually kick off an ad hoc run. You can monitor all of your pipeline runs natively in the Azure Data Factory user experience. To open the monitoring experience, select the Monitor & Manage tile in the data factory blade of the Azure portal. If you're already in the ADF UX, click on the Monitor icon on the left sidebar.

By default, all data factory runs are displayed in the browser's local time zone. If you change the time zone, all the date/time fields snap to the one that you selected.

Monitor pipeline runs

The default monitoring view is list of triggered pipeline runs in the selected time period. You can change the time range and filter by status, pipeline name, or annotation. Hover over the specific pipeline run to get run-specific actions such as rerun and the consumption report.

List view for monitoring pipeline runs

The pipeline run grid contains the following columns:

Column name Description
Pipeline Name Name of the pipeline
Run Start Start date and time for the pipeline run (MM/DD/YYYY, HH:MM:SS AM/PM)
Run End End date and time for the pipeline run (MM/DD/YYYY, HH:MM:SS AM/PM)
Duration Run duration (HH:MM:SS)
Triggered By The name of the trigger that started the pipeline
Status Failed, Succeeded, In Progress, Canceled, or Queued
Annotations Filterable tags associated with a pipeline
Parameters Parameters for the pipeline run (name/value pairs)
Error If the pipeline failed, the run error
Run ID ID of the pipeline run

You need to manually select the Refresh button to refresh the list of pipeline and activity runs. Autorefresh is currently not supported.

Refresh button

To view the results of a debug run, select the Debug tab.

Select the View active debug runs icon

Monitor activity runs

To get a detailed view of the individual activity runs of a specific pipeline run, click on the pipeline name.

View activity runs

The list view shows activity runs that correspond to each pipeline run. Hover over the specific activity run to get run-specific information such as the JSON input, JSON output, and detailed activity-specific monitoring experiences.

There is information about SalesAnalyticsMLPipeline, followed by a list of activity runs.

Column name Description
Activity Name Name of the activity inside the pipeline
Activity Type Type of the activity, such as Copy, ExecuteDataFlow, or AzureMLExecutePipeline
Actions Icons that allow you to see JSON input information, JSON output information, or detailed activity-specific monitoring experiences
Run Start Start date and time for the activity run (MM/DD/YYYY, HH:MM:SS AM/PM)
Duration Run duration (HH:MM:SS)
Status Failed, Succeeded, In Progress, or Canceled
Integration Runtime Which Integration Runtime the activity was run on
User Properties User-defined properties of the activity
Error If the activity failed, the run error
Run ID ID of the activity run

If an activity failed, you can see the detailed error message by clicking on the icon in the error column.

A notification appears with error details including error code, failure type, and error details.

Promote user properties to monitor

Promote any pipeline activity property as a user property so that it becomes an entity that you monitor. For example, you can promote the Source and Destination properties of the copy activity in your pipeline as user properties.


You can only promote up to five pipeline activity properties as user properties.

Create user properties

After you create the user properties, you can monitor them in the monitoring list views.

Add columns for user properties to the activity runs list

If the source for the copy activity is a table name, you can monitor the source table name as a column in the list view for activity runs.

Activity runs list with columns for user properties

Rerun pipelines and activities

Rerun behavior of the container activities is as follows:

  • Wait- Activity will behave as before.
  • Set Variable - Activity will behave as before.
  • Filter - Activity will behave as before.
  • Until Activity will evaluate the expression and will loop until the condition is satisfied. Inner activities may still be skipped based on the rerun rules.
  • Foreach Activity will always loop on the items it receives. Inner activities may still be skipped based on the rerun rules.
  • If and switch - Conditions will always be evaluated. Inner activities may still be skipped based on the rerun rules.
  • Execute pipeline activity - The child pipeline will be triggered, but all activities in the child pipeline may still be skipped based on the rerun rules.

To rerun a pipeline that has previously ran from the start, hover over the specific pipeline run and select Rerun. If you select multiple pipelines, you can use the Rerun button to run them all.

Rerun a pipeline

If you wish to rerun starting at a specific point, you can do so from the activity runs view. Select the activity you wish to start from and select Rerun from activity.

Rerun an activity run

Rerun from failed activity

If an activity fails, times out, or is canceled, you can rerun the pipeline from that failed activity by selecting Rerun from failed activity.

Rerun failed activity

View rerun history

You can view the rerun history for all the pipeline runs in the list view.

View history

You can also view rerun history for a particular pipeline run.

View history for a pipeline run

Monitor consumption

You can see the resources consumed by a pipeline run by clicking the consumption icon next to the run.

Screenshot that shows where you can see the resources consumed by a pipeline.

Clicking the icon opens a consumption report of resources used by that pipeline run.

Monitor consumption

You can plug these values into the Azure pricing calculator to estimate the cost of the pipeline run. For more information on Azure Data Factory pricing, see Understanding pricing.


These values returned by the pricing calculator is an estimate. It doesn't reflect the exact amount you will be billed by Azure Data Factory

Gantt views

A Gantt chart is a view that allows you to see the run history over a time range. By switching to a Gantt view, you will see all pipeline runs grouped by name displayed as bars relative to how long the run took. You can also group by annotations/tags that you've create on your pipeline. The Gantt view is also available at the activity run level.

Example of a Gantt chart

The length of the bar informs the duration of the pipeline. You can also select the bar to see more details.

Gantt chart duration


You can raise alerts on supported metrics in Data Factory. Select Monitor > Alerts & metrics on the Data Factory monitoring page to get started.

Data factory Monitor page

For a seven-minute introduction and demonstration of this feature, watch the following video:

Create alerts

  1. Select New alert rule to create a new alert.

    New Alert Rule button

  2. Specify the rule name and select the alert severity.

    Boxes for rule name and severity

  3. Select the alert criteria.

    Box for target criteria

    Screenshot that shows where you select one metric to set up the alert condition.

    List of criteria

    You can create alerts on various metrics, including those for ADF entity count/size, activity/pipeline/trigger runs, Integration Runtime (IR) CPU utilization/memory/node count/queue, as well as for SSIS package executions and SSIS IR start/stop operations.

  4. Configure the alert logic. You can create an alert for the selected metric for all pipelines and corresponding activities. You can also select a particular activity type, activity name, pipeline name, or failure type.

    Options for configuring alert logic

  5. Configure email, SMS, push, and voice notifications for the alert. Create an action group, or choose an existing one, for the alert notifications.

    Options for configuring notifications

    Options for adding a notification

  6. Create the alert rule.

    Options for creating an alert rule

Next steps

To learn about monitoring and managing pipelines, see the Monitor and manage pipelines programmatically article.