quickstart-create-data-factory-powershell.md

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Copy data in Blob Storage using Azure Data Factory	Create an Azure Data Factory using PowerShell to copy data from one location in Azure Blob storage to another location.	jianleishen	data-movement	powershell	quickstart	05/15/2024	jianleishen	devx-track-azurepowershell, mode-api

Quickstart: Create an Azure Data Factory using PowerShell

[!INCLUDEappliesto-adf-xxx-md]

This quickstart describes how to use PowerShell to create an Azure Data Factory. The pipeline you create in this data factory copies data from one folder to another folder in an Azure blob storage. For a tutorial on how to transform data using Azure Data Factory, see Tutorial: Transform data using Spark.

Note

This article does not provide a detailed introduction of the Data Factory service. For an introduction to the Azure Data Factory service, see Introduction to Azure Data Factory.

[!INCLUDE data-factory-quickstart-prerequisites]

Azure PowerShell

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Install the latest Azure PowerShell modules by following instructions in How to install and configure Azure PowerShell.

Warning

If you do not use latest versions of PowerShell and Data Factory module, you may run into deserialization errors while running the commands.

Log in to PowerShell

1. Launch PowerShell on your machine. Keep PowerShell open until the end of this quickstart. If you close and reopen, you need to run these commands again.

2. Run the following command, and enter the same Azure user name and password that you use to sign in to the Azure portal:

   Connect-AzAccount

3. Run the following command to view all the subscriptions for this account:

   Get-AzSubscription

4. If you see multiple subscriptions associated with your account, run the following command to select the subscription that you want to work with. Replace SubscriptionId with the ID of your Azure subscription:

   Select-AzSubscription -SubscriptionId "<SubscriptionId>"

Create a data factory

1. Define a variable for the resource group name that you use in PowerShell commands later. Copy the following command text to PowerShell, specify a name for the Azure resource group in double quotes, and then run the command. For example: "ADFQuickStartRG".

   $resourceGroupName = "ADFQuickStartRG";

   If the resource group already exists, you may not want to overwrite it. Assign a different value to the $ResourceGroupName variable and run the command again

2. To create the Azure resource group, run the following command:

   $ResGrp = New-AzResourceGroup $resourceGroupName -location 'East US'

   If the resource group already exists, you may not want to overwrite it. Assign a different value to the $ResourceGroupName variable and run the command again.

3. Define a variable for the data factory name.

   [!IMPORTANT] Update the data factory name to be globally unique. For example, ADFTutorialFactorySP1127.

   $dataFactoryName = "ADFQuickStartFactory";

4. To create the data factory, run the following Set-AzDataFactoryV2 cmdlet, using the Location and ResourceGroupName property from the $ResGrp variable:

   $DataFactory = Set-AzDataFactoryV2 -ResourceGroupName $ResGrp.ResourceGroupName `
       -Location $ResGrp.Location -Name $dataFactoryName

Note the following points:

• The name of the Azure Data Factory must be globally unique. If you receive the following error, change the name and try again.

  The specified Data Factory name 'ADFv2QuickStartDataFactory' is already in use. Data Factory names must be globally unique.
  

• To create Data Factory instances, the user account you use to log in to Azure must be a member of contributor or owner roles, or an administrator of the Azure subscription.

• For a list of Azure regions in which Data Factory is currently available, select the regions that interest you on the following page, and then expand Analytics to locate Data Factory: Products available by region. The data stores (Azure Storage, Azure SQL Database, etc.) and computes (HDInsight, etc.) used by data factory can be in other regions.

Create a linked service

Create linked services in a data factory to link your data stores and compute services to the data factory. In this quickstart, you create an Azure Storage linked service that is used as both the source and sink stores. The linked service has the connection information that the Data Factory service uses at runtime to connect to it.

Tip

In this quickstart, you use Account key as the authentication type for your data store, but you can choose other supported authentication methods: SAS URI,Service Principal and Managed Identity if needed. Refer to corresponding sections in this article for details. To store secrets for data stores securely, it's also recommended to use an Azure Key Vault. Refer to this article for detailed illustrations.

1. Create a JSON file named AzureStorageLinkedService.json in C:\ADFv2QuickStartPSH folder with the following content: (Create the folder ADFv2QuickStartPSH if it does not already exist.).

   [!IMPORTANT] Replace <accountName> and <accountKey> with name and key of your Azure storage account before saving the file.

   {
       "name": "AzureStorageLinkedService",
       "properties": {
           "annotations": [],
           "type": "AzureBlobStorage",
           "typeProperties": {
               "connectionString": "DefaultEndpointsProtocol=https;AccountName=<accountName>;AccountKey=<accountKey>;EndpointSuffix=core.windows.net"
           }
       }
   }

   If you are using Notepad, select All files for the Save as type filed in the Save as dialog box. Otherwise, it may add .txt extension to the file. For example, AzureStorageLinkedService.json.txt. If you create the file in File Explorer before opening it in Notepad, you may not see the .txt extension since the Hide extensions for known files types option is set by default. Remove the .txt extension before proceeding to the next step.

2. In PowerShell, switch to the ADFv2QuickStartPSH folder.

   Set-Location 'C:\ADFv2QuickStartPSH'

3. Run the Set-AzDataFactoryV2LinkedService cmdlet to create the linked service: AzureStorageLinkedService.

   Set-AzDataFactoryV2LinkedService -DataFactoryName $DataFactory.DataFactoryName `
       -ResourceGroupName $ResGrp.ResourceGroupName -Name "AzureStorageLinkedService" `
       -DefinitionFile ".\AzureStorageLinkedService.json"

   Here is the sample output:

   LinkedServiceName : AzureStorageLinkedService
   ResourceGroupName : <resourceGroupName>
   DataFactoryName   : <dataFactoryName>
   Properties        : Microsoft.Azure.Management.DataFactory.Models.AzureBlobStorageLinkedService

Create datasets

In this procedure, you create two datasets: InputDataset and OutputDataset. These datasets are of type Binary. They refer to the Azure Storage linked service that you created in the previous section. The input dataset represents the source data in the input folder. In the input dataset definition, you specify the blob container (adftutorial), the folder (input), and the file (emp.txt) that contain the source data. The output dataset represents the data that's copied to the destination. In the output dataset definition, you specify the blob container (adftutorial), the folder (output), and the file to which the data is copied.

1. Create a JSON file named InputDataset.json in the C:\ADFv2QuickStartPSH folder, with the following content:

   {
       "name": "InputDataset",
       "properties": {
           "linkedServiceName": {
               "referenceName": "AzureStorageLinkedService",
               "type": "LinkedServiceReference"
           },
           "annotations": [],
           "type": "Binary",
           "typeProperties": {
               "location": {
                   "type": "AzureBlobStorageLocation",
                   "fileName": "emp.txt",
                   "folderPath": "input",
                   "container": "adftutorial"
               }
           }
       }
   }

2. To create the dataset: InputDataset, run the Set-AzDataFactoryV2Dataset cmdlet.

   Set-AzDataFactoryV2Dataset -DataFactoryName $DataFactory.DataFactoryName `
       -ResourceGroupName $ResGrp.ResourceGroupName -Name "InputDataset" `
       -DefinitionFile ".\InputDataset.json"

   Here is the sample output:

   DatasetName       : InputDataset
   ResourceGroupName : <resourceGroupname>
   DataFactoryName   : <dataFactoryName>
   Structure         :
   Properties        : Microsoft.Azure.Management.DataFactory.Models.BinaryDataset

3. Repeat the steps to create the output dataset. Create a JSON file named OutputDataset.json in the C:\ADFv2QuickStartPSH folder, with the following content:

   {
       "name": "OutputDataset",
       "properties": {
           "linkedServiceName": {
               "referenceName": "AzureStorageLinkedService",
               "type": "LinkedServiceReference"
           },
           "annotations": [],
           "type": "Binary",
           "typeProperties": {
               "location": {
                   "type": "AzureBlobStorageLocation",
                   "folderPath": "output",
                   "container": "adftutorial"
               }
           }
       }
   }

4. Run the Set-AzDataFactoryV2Dataset cmdlet to create the OutDataset.

   Set-AzDataFactoryV2Dataset -DataFactoryName $DataFactory.DataFactoryName `
       -ResourceGroupName $ResGrp.ResourceGroupName -Name "OutputDataset" `
       -DefinitionFile ".\OutputDataset.json"

   Here is the sample output:

   DatasetName       : OutputDataset
   ResourceGroupName : <resourceGroupname>
   DataFactoryName   : <dataFactoryName>
   Structure         :
   Properties        : Microsoft.Azure.Management.DataFactory.Models.BinaryDataset

Create a pipeline

In this procedure, you create a pipeline with a copy activity that uses the input and output datasets. The copy activity copies data from the file you specified in the input dataset settings to the file you specified in the output dataset settings.

1. Create a JSON file named Adfv2QuickStartPipeline.json in the C:\ADFv2QuickStartPSH folder with the following content:

   {
       "name": "Adfv2QuickStartPipeline",
       "properties": {
           "activities": [
               {
                   "name": "CopyFromBlobToBlob",
                   "type": "Copy",
                   "dependsOn": [],
                   "policy": {
                       "timeout": "7.00:00:00",
                       "retry": 0,
                       "retryIntervalInSeconds": 30,
                       "secureOutput": false,
                       "secureInput": false
                   },
                   "userProperties": [],
                   "typeProperties": {
                       "source": {
                           "type": "BinarySource",
                           "storeSettings": {
                               "type": "AzureBlobStorageReadSettings",
                               "recursive": true
                           }
                       },
                       "sink": {
                           "type": "BinarySink",
                           "storeSettings": {
                               "type": "AzureBlobStorageWriteSettings"
                           }
                       },
                       "enableStaging": false
                   },
                   "inputs": [
                       {
                           "referenceName": "InputDataset",
                           "type": "DatasetReference"
                       }
                   ],
                   "outputs": [
                       {
                           "referenceName": "OutputDataset",
                           "type": "DatasetReference"
                       }
                   ]
               }
           ],
           "annotations": []
       }
   }

2. To create the pipeline: Adfv2QuickStartPipeline, Run the Set-AzDataFactoryV2Pipeline cmdlet.

   $DFPipeLine = Set-AzDataFactoryV2Pipeline `
       -DataFactoryName $DataFactory.DataFactoryName `
       -ResourceGroupName $ResGrp.ResourceGroupName `
       -Name "Adfv2QuickStartPipeline" `
       -DefinitionFile ".\Adfv2QuickStartPipeline.json"

Create a pipeline run

In this step, you create a pipeline run.

Run the Invoke-AzDataFactoryV2Pipeline cmdlet to create a pipeline run. The cmdlet returns the pipeline run ID for future monitoring.

$RunId = Invoke-AzDataFactoryV2Pipeline `
  -DataFactoryName $DataFactory.DataFactoryName `
  -ResourceGroupName $ResGrp.ResourceGroupName `
  -PipelineName $DFPipeLine.Name 

Monitor the pipeline run

1. Run the following PowerShell script to continuously check the pipeline run status until it finishes copying the data. Copy/paste the following script in the PowerShell window, and press ENTER.

   while ($True) {
       $Run = Get-AzDataFactoryV2PipelineRun `
           -ResourceGroupName $ResGrp.ResourceGroupName `
           -DataFactoryName $DataFactory.DataFactoryName `
           -PipelineRunId $RunId
   
       if ($Run) {
           if ( ($Run.Status -ne "InProgress") -and ($Run.Status -ne "Queued") ) {
               Write-Output ("Pipeline run finished. The status is: " +  $Run.Status)
               $Run
               break
           }
           Write-Output ("Pipeline is running...status: " + $Run.Status)
       }
   
       Start-Sleep -Seconds 10
   }

   Here is the sample output of pipeline run:

   Pipeline is running...status: InProgress
   Pipeline run finished. The status is:  Succeeded
   
   ResourceGroupName : ADFQuickStartRG
   DataFactoryName   : ADFQuickStartFactory
   RunId             : 00000000-0000-0000-0000-0000000000000
   PipelineName      : Adfv2QuickStartPipeline
   LastUpdated       : 8/27/2019 7:23:07 AM
   Parameters        : {}
   RunStart          : 8/27/2019 7:22:56 AM
   RunEnd            : 8/27/2019 7:23:07 AM
   DurationInMs      : 11324
   Status            : Succeeded
   Message           : 

2. Run the following script to retrieve copy activity run details, for example, size of the data read/written.

   Write-Output "Activity run details:"
   $Result = Get-AzDataFactoryV2ActivityRun -DataFactoryName $DataFactory.DataFactoryName -ResourceGroupName $ResGrp.ResourceGroupName -PipelineRunId $RunId -RunStartedAfter (Get-Date).AddMinutes(-30) -RunStartedBefore (Get-Date).AddMinutes(30)
   $Result
   
   Write-Output "Activity 'Output' section:"
   $Result.Output -join "`r`n"
   
   Write-Output "Activity 'Error' section:"
   $Result.Error -join "`r`n"

3. Confirm that you see the output similar to the following sample output of activity run result:

   ResourceGroupName : ADFQuickStartRG
   DataFactoryName   : ADFQuickStartFactory
   ActivityRunId     : 00000000-0000-0000-0000-000000000000
   ActivityName      : CopyFromBlobToBlob
   PipelineRunId     : 00000000-0000-0000-0000-000000000000
   PipelineName      : Adfv2QuickStartPipeline
   Input             : {source, sink, enableStaging}
   Output            : {dataRead, dataWritten, filesRead, filesWritten...}
   LinkedServiceName :
   ActivityRunStart  : 8/27/2019 7:22:58 AM
   ActivityRunEnd    : 8/27/2019 7:23:05 AM
   DurationInMs      : 6828
   Status            : Succeeded
   Error             : {errorCode, message, failureType, target}
   
   Activity 'Output' section:
   "dataRead": 20
   "dataWritten": 20
   "filesRead": 1
   "filesWritten": 1
   "sourcePeakConnections": 1
   "sinkPeakConnections": 1
   "copyDuration": 4
   "throughput": 0.01
   "errors": []
   "effectiveIntegrationRuntime": "DefaultIntegrationRuntime (Central US)"
   "usedDataIntegrationUnits": 4
   "usedParallelCopies": 1
   "executionDetails": [
     {
       "source": {
         "type": "AzureBlobStorage"
       },
       "sink": {
         "type": "AzureBlobStorage"
       },
       "status": "Succeeded",
       "start": "2019-08-27T07:22:59.1045645Z",
       "duration": 4,
       "usedDataIntegrationUnits": 4,
       "usedParallelCopies": 1,
       "detailedDurations": {
         "queuingDuration": 3,
         "transferDuration": 1
       }
     }
   ]
   
   Activity 'Error' section:
   "errorCode": ""
   "message": ""
   "failureType": ""
   "target": "CopyFromBlobToBlob"

[!INCLUDE data-factory-quickstart-verify-output-cleanup.md]

Related content

The pipeline in this sample copies data from one location to another location in an Azure blob storage. Go through the tutorials to learn about using Data Factory in more scenarios.