Monitor and manage Azure Data Factory pipelines by using the Azure portal and PowerShell


This article applies to version 1 of Data Factory. If you are using the current version of the Data Factory service, see monitor and manage Data Factory pipelines in.

This article describes how to monitor, manage, and debug your pipelines by using Azure portal and PowerShell.


The monitoring & management application provides a better support for monitoring and managing your data pipelines, and troubleshooting any issues. For details about using the application, see monitor and manage Data Factory pipelines by using the Monitoring and Management app.


Azure Data Factory version 1 now uses the new Azure Monitor alerting infrastructure. The old alerting infrastructure is deprecated. As a result, your existing alerts configured for version 1 data factories no longer work. Your existing alerts for v1 data factories are not migrated automatically. You have to recreate these alerts on the new alerting infrastructure. Log in to the Azure portal and select Monitor to create new alerts on metrics (such as failed runs or successful runs) for your version 1 data factories.


We recommend that you use the Azure Az PowerShell module to interact with Azure. See Install Azure PowerShell to get started. To learn how to migrate to the Az PowerShell module, see Migrate Azure PowerShell from AzureRM to Az.

Understand pipelines and activity states

By using the Azure portal, you can:

  • View your data factory as a diagram.
  • View activities in a pipeline.
  • View input and output datasets.

This section also describes how a dataset slice transitions from one state to another state.

  1. Sign in to the Azure portal.

  2. Click Data factories on the menu on the left. If you don't see it, click More services >, and then click Data factories under the INTELLIGENCE + ANALYTICS category.

    Browse all > Data factories

  3. On the Data factories blade, select the data factory that you're interested in.

    Select data factory

    You should see the home page for the data factory.

    Data factory blade

Diagram view of your data factory

The Diagram view of a data factory provides a single pane of glass to monitor and manage the data factory and its assets. To see the Diagram view of your data factory, click Diagram on the home page for the data factory.

Diagram view

You can zoom in, zoom out, zoom to fit, zoom to 100%, lock the layout of the diagram, and automatically position pipelines and datasets. You can also see the data lineage information (that is, show upstream and downstream items of selected items).

Activities inside a pipeline

  1. Right-click the pipeline, and then click Open pipeline to see all activities in the pipeline, along with input and output datasets for the activities. This feature is useful when your pipeline includes more than one activity and you want to understand the operational lineage of a single pipeline.

    Open pipeline menu

  2. In the following example, you see a copy activity in the pipeline with an input and an output.

    Activities inside a pipeline

  3. You can navigate back to the home page of the data factory by clicking the Data factory link in the breadcrumb at the top-left corner.

    Navigate back to data factory

View the state of each activity inside a pipeline

You can view the current state of an activity by viewing the status of any of the datasets that are produced by the activity.

By double-clicking the OutputBlobTable in the Diagram, you can see all the slices that are produced by different activity runs inside a pipeline. You can see that the copy activity ran successfully for the last eight hours and produced the slices in the Ready state.

State of the pipeline

The dataset slices in the data factory can have one of the following statuses:

WaitingScheduleTimeThe time hasn't come for the slice to run.
DatasetDependenciesThe upstream dependencies aren't ready.
ComputeResourcesThe compute resources aren't available.
ConcurrencyLimit All the activity instances are busy running other slices.
ActivityResumeThe activity is paused and can't run the slices until the activity is resumed.
RetryActivity execution is being retried.
ValidationValidation hasn't started yet.
ValidationRetryValidation is waiting to be retried.
InProgressValidatingValidation is in progress.
- The slice is being processed.
FailedTimedOutThe activity execution took longer than what is allowed by the activity.
CanceledThe slice was canceled by user action.
ValidationValidation has failed.
-The slice failed to be generated and/or validated.
Ready-The slice is ready for consumption.
SkippedNoneThe slice isn't being processed.
None-A slice used to exist with a different status, but it has been reset.

You can view the details about a slice by clicking a slice entry on the Recently Updated Slices blade.

Slice details

If the slice has been executed multiple times, you see multiple rows in the Activity runs list. You can view details about an activity run by clicking the run entry in the Activity runs list. The list shows all the log files, along with an error message if there is one. This feature is useful to view and debug logs without having to leave your data factory.

Activity run details

If the slice isn't in the Ready state, you can see the upstream slices that aren't ready and are blocking the current slice from executing in the Upstream slices that are not ready list. This feature is useful when your slice is in Waiting state and you want to understand the upstream dependencies that the slice is waiting on.

Upstream slices that are not ready

Dataset state diagram

After you deploy a data factory and the pipelines have a valid active period, the dataset slices transition from one state to another. Currently, the slice status follows the following state diagram:

State diagram

The dataset state transition flow in data factory is the following: Waiting -> In-Progress/In-Progress (Validating) -> Ready/Failed.

The slice starts in a Waiting state, waiting for preconditions to be met before it executes. Then, the activity starts executing, and the slice goes into an In-Progress state. The activity execution might succeed or fail. The slice is marked as Ready or Failed, based on the result of the execution.

You can reset the slice to go back from the Ready or Failed state to the Waiting state. You can also mark the slice state to Skip, which prevents the activity from executing and not processing the slice.

Pause and resume pipelines

You can manage your pipelines by using Azure PowerShell. For example, you can pause and resume pipelines by running Azure PowerShell cmdlets.


The diagram view does not support pausing and resuming pipelines. If you want to use a user interface, use the monitoring and managing application. For details about using the application, see monitor and manage Data Factory pipelines by using the Monitoring and Management app article.

You can pause/suspend pipelines by using the Suspend-AzDataFactoryPipeline PowerShell cmdlet. This cmdlet is useful when you don't want to run your pipelines until an issue is fixed.

Suspend-AzDataFactoryPipeline [-ResourceGroupName] <String> [-DataFactoryName] <String> [-Name] <String>

For example:

Suspend-AzDataFactoryPipeline -ResourceGroupName ADF -DataFactoryName productrecgamalbox1dev -Name PartitionProductsUsagePipeline

After the issue has been fixed with the pipeline, you can resume the suspended pipeline by running the following PowerShell command:

Resume-AzDataFactoryPipeline [-ResourceGroupName] <String> [-DataFactoryName] <String> [-Name] <String>

For example:

Resume-AzDataFactoryPipeline -ResourceGroupName ADF -DataFactoryName productrecgamalbox1dev -Name PartitionProductsUsagePipeline

Debug pipelines

Azure Data Factory provides rich capabilities for you to debug and troubleshoot pipelines by using the Azure portal and Azure PowerShell.


It is much easier to troubleshot errors using the Monitoring & Management App. For details about using the application, see monitor and manage Data Factory pipelines by using the Monitoring and Management app article.

Find errors in a pipeline

If the activity run fails in a pipeline, the dataset that is produced by the pipeline is in an error state because of the failure. You can debug and troubleshoot errors in Azure Data Factory by using the following methods.

Use the Azure portal to debug an error

  1. On the Table blade, click the problem slice that has the Status set to Failed.

    Table blade with problem slice

  2. On the Data slice blade, click the activity run that failed.

    Data slice with an error

  3. On the Activity run details blade, you can download the files that are associated with the HDInsight processing. Click Download for Status/stderr to download the error log file that contains details about the error.

    Activity run details blade with error

Use PowerShell to debug an error

  1. Launch PowerShell.

  2. Run the Get-AzDataFactorySlice command to see the slices and their statuses. You should see a slice with the status of Failed.

    Get-AzDataFactorySlice [-ResourceGroupName] <String> [-DataFactoryName] <String> [-DatasetName] <String> [-StartDateTime] <DateTime> [[-EndDateTime] <DateTime> ] [-Profile <AzureProfile> ] [ <CommonParameters>]

    For example:

    Get-AzDataFactorySlice -ResourceGroupName ADF -DataFactoryName LogProcessingFactory -DatasetName EnrichedGameEventsTable -StartDateTime 2014-05-04 20:00:00

    Replace StartDateTime with start time of your pipeline.

  3. Now, run the Get-AzDataFactoryRun cmdlet to get details about the activity run for the slice.

     Get-AzDataFactoryRun [-ResourceGroupName] <String> [-DataFactoryName] <String> [-DatasetName] <String> [-StartDateTime]
     <DateTime> [-Profile <AzureProfile> ] [ <CommonParameters>]

    For example:

    Get-AzDataFactoryRun -ResourceGroupName ADF -DataFactoryName LogProcessingFactory -DatasetName EnrichedGameEventsTable -StartDateTime "5/5/2014 12:00:00 AM"

    The value of StartDateTime is the start time for the error/problem slice that you noted from the previous step. The date-time should be enclosed in double quotes.

  4. You should see output with details about the error that is similar to the following:

    Id                      : 841b77c9-d56c-48d1-99a3-8c16c3e77d39
    ResourceGroupName       : ADF
    DataFactoryName         : LogProcessingFactory3
    DatasetName               : EnrichedGameEventsTable
    ProcessingStartTime     : 10/10/2014 3:04:52 AM
    ProcessingEndTime       : 10/10/2014 3:06:49 AM
    PercentComplete         : 0
    DataSliceStart          : 5/5/2014 12:00:00 AM
    DataSliceEnd            : 5/6/2014 12:00:00 AM
    Status                  : FailedExecution
    Timestamp               : 10/10/2014 3:04:52 AM
    RetryAttempt            : 0
    Properties              : {}
    ErrorMessage            : Pig script failed with exit code '5'. See wasb://
                8c16c3e77d39/10_10_2014_03_04_53_277/Status/stderr' for
                more details.
    ActivityName            : PigEnrichLogs
    PipelineName            : EnrichGameLogsPipeline
    Type                    :
  5. You can run the Save-AzDataFactoryLog cmdlet with the Id value that you see from the output, and download the log files by using the -DownloadLogsoption for the cmdlet.

    Save-AzDataFactoryLog -ResourceGroupName "ADF" -DataFactoryName "LogProcessingFactory" -Id "841b77c9-d56c-48d1-99a3-8c16c3e77d39" -DownloadLogs -Output "C:\Test"

Rerun failures in a pipeline


It's easier to troubleshoot errors and rerun failed slices by using the Monitoring & Management App. For details about using the application, see monitor and manage Data Factory pipelines by using the Monitoring and Management app.

Use the Azure portal

After you troubleshoot and debug failures in a pipeline, you can rerun failures by navigating to the error slice and clicking the Run button on the command bar.

Rerun a failed slice

In case the slice has failed validation because of a policy failure (for example, if data isn't available), you can fix the failure and validate again by clicking the Validate button on the command bar.

Fix errors and validate

Use Azure PowerShell

You can rerun failures by using the Set-AzDataFactorySliceStatus cmdlet. See the Set-AzDataFactorySliceStatus topic for syntax and other details about the cmdlet.


The following example sets the status of all slices for the table 'DAWikiAggregatedData' to 'Waiting' in the Azure data factory 'WikiADF'.

The 'UpdateType' is set to 'UpstreamInPipeline', which means that statuses of each slice for the table and all the dependent (upstream) tables are set to 'Waiting'. The other possible value for this parameter is 'Individual'.

Set-AzDataFactorySliceStatus -ResourceGroupName ADF -DataFactoryName WikiADF -DatasetName DAWikiAggregatedData -Status Waiting -UpdateType UpstreamInPipeline -StartDateTime 2014-05-21T16:00:00 -EndDateTime 2014-05-21T20:00:00

Create alerts in the Azure portal

  1. Log in to the Azure portal and select Monitor -> Alerts to open the Alerts page.

    Open the Alerts page.

  2. Select + New Alert rule to create a new alert.

    Create a new alert

  3. Define the Alert condition. (Make sure to select Data factories in the Filter by resource type field.) You can also specify values for Dimensions.

    Define the Alert Condition - Select target

    Define the Alert Condition - Add alert criteria

    Define the Alert Condition - Add alert logic

  4. Define the Alert details.

    Define the Alert Details

  5. Define the Action group.

    Define the Action Group - create a new Action group

    Define the Action Group - set properties

    Define the Action Group - new action group created

Move a data factory to a different resource group or subscription

You can move a data factory to a different resource group or a different subscription by using the Move command bar button on the home page of your data factory.

Move data factory

You can also move any related resources (such as alerts that are associated with the data factory), along with the data factory.

Move resources dialog box