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PowerShell script - transform data in cloud using Azure Data Factory

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This sample PowerShell script creates a pipeline that transforms data in the cloud by running Spark program on an Azure HDInsight Spark cluster.

Note

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

This sample requires Azure PowerShell. Run Get-Module -ListAvailable Az to find the version. If you need to install or upgrade, see Install Azure PowerShell module.

Run the Connect-AzAccount cmdlet to connect to Azure.

Prerequisites

  • Azure Storage account. Create a Python script and an input file, and upload them to the Azure storage. The output from the spark program is stored in this storage account. The on-demand Spark cluster uses the same storage account as its primary storage.

Upload Python script to your Blob Storage account

  1. Create a Python file named WordCount_Spark.py with the following content:

    import sys
from operator import add

from pyspark.sql import SparkSession

def main():
    spark = SparkSession\
        .builder\
        .appName("PythonWordCount")\
        .getOrCreate()

    lines = spark.read.text("wasbs://adftutorial@<storageaccountname>.blob.core.windows.net/spark/inputfiles/minecraftstory.txt").rdd.map(lambda r: r[0])
    counts = lines.flatMap(lambda x: x.split(' ')) \
        .map(lambda x: (x, 1)) \
        .reduceByKey(add)
    counts.saveAsTextFile("wasbs://adftutorial@<storageaccountname>.blob.core.windows.net/spark/outputfiles/wordcount")

    spark.stop()

if __name__ == "__main__":
    main()

  2. Replace <storageAccountName> with the name of your Azure Storage account. Then, save the file.

  3. In your Azure Blob Storage, create a container named adftutorial if it does not exist.

  4. Create a folder named spark.

  5. Create a subfolder named script under spark folder.

  6. Upload the WordCount_Spark.py file to the script subfolder.

Upload the input file

  1. Create a file named minecraftstory.txt with some text. The spark program counts the number of words in this text.
  2. Create a subfolder named inputfiles in the spark folder of the blob container.
  3. Upload the minecraftstory.txt to the inputfiles subfolder.

Sample script

Important

This script creates JSON files that define Data Factory entities (linked service, dataset, and pipeline) on your hard drive in the c:\ folder.

powershell Set-ExecutionPolicy Unrestricted -Scope CurrentUser

# Set variables with your own values
$resourceGroupName = "<Azure resource group name>"
$dataFactoryName = "<Data factory name. Must be globally unique.>"
$dataFactoryRegion = "East US" 
$storageAccountName = "<Az.Storage account name> "
$storageAccountKey = "<Az.Storage account key>"
$subscriptionID = "<Azure subscription ID>"
$tenantID = "<tenant ID>"
$servicePrincipalID = "<Active directory service principal ID>"
$servicePrincipalKey = "<Active directory service principal key>"

$pipelineName = "SparkTransformPipeline"

# Create a resource group
New-AzResourceGroup -Name $resourceGroupName -Location $dataFactoryRegion

# Create a data factory
$df = Set-AzDataFactory -ResourceGroupName $resourceGroupName -Location $dataFactoryRegion -Name $dataFactoryName

# Create an Az.Storage linked service in the data factory

## JSON definition of the linked service. 
$storageLinkedServiceDefinition = @"
{
    "name": "AzureStorageLinkedService",
    "properties": {
        "type": "AzureStorage",
        "typeProperties": {
            "connectionString": {
                "value": "DefaultEndpointsProtocol=https;AccountName=$storageAccountName;AccountKey=$storageAccountKey",
                "type": "SecureString"
            }
        }
    }
}
"@

## IMPORTANT: store the JSON definition in a file that will be used by the Set-AzDataFactoryLinkedService command. 
$storageLinkedServiceDefinition | Out-File c:\AzureStorageLinkedService.json

## Creates an Az.Storage linked service
Set-AzDataFactoryLinkedService -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -Name "AzureStorageLinkedService" -File c:\AzureStorageLinkedService.json

# Create on-demand Spark linked service in the data factory

## JSON definition of the linked service. 
$sparkLinkedServiceDefinition = @"
{
    "name": "OnDemandSparkLinkedService",
    "properties": {
      "type": "HDInsightOnDemand",
      "typeProperties": {
        "clusterSize": 2,
        "clusterType": "spark",
        "timeToLive": "00:15:00",
        "hostSubscriptionId": "$subscriptionID",
        "servicePrincipalId": "$servicePrincipalID",
        "servicePrincipalKey": {
          "value": "$servicePrincipalKey",
          "type": "SecureString"
        },
        "tenant": "$tenantID",
        "clusterResourceGroup": "$resourceGroupName",
        "version": "3.6",
        "osType": "Linux",
        "clusterNamePrefix":"ADFSparkSample",
        "linkedServiceName": {
          "referenceName": "AzureStorageLinkedService",
          "type": "LinkedServiceReference"
        }
      }
    }
}
"@

## IMPORTANT: store the JSON definition in a file that will be used by the Set-AzDataFactoryLinkedService command. 
$sparkLinkedServiceDefinition | Out-File c:\OnDemandSparkLinkedService.json

# Creates an on-demand Spark linked service
Set-AzDataFactoryLinkedService -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -Name "OnDemandSparkLinkedService" -File "C:\OnDemandSparkLinkedService.json"

# Create a pipeline in the data factory

## JSON definition of the pipeline
$pipelineDefinition = @"
{
  "name": "SparkTransformPipeline",
  "properties": {
    "activities": [
      {
        "name": "MySparkActivity",
        "type": "HDInsightSpark",
        "linkedServiceName": {
            "referenceName": "OnDemandSparkLinkedService",
            "type": "LinkedServiceReference"
        },
        "typeProperties": {
          "rootPath": "adftutorial/spark",
          "entryFilePath": "script/WordCount_Spark.py",
          "getDebugInfo": "Failure",
          "sparkJobLinkedService": {
            "referenceName": "AzureStorageLinkedService",
            "type": "LinkedServiceReference"
          }
        }
      }
    ]
  }
}
"@

## IMPORTANT: store the JSON definition in a file that will be used by the Set-AzDataFactoryPipeline command.
$pipelineDefinition | Out-File c:\SparkTransformPipeline.json

## Create a pipeline with Spark Activity in the data factory
Set-AzDataFactoryPipeline -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -Name "SparkTransformPipeline" -File "c:\SparkTransformPipeline.json"

# Create a pipeline run 

## JSON definition for dummy pipeline parameters
$pipelineParameters = @"
{
    "dummy":  "b"
}
"@

## IMPORTANT: store the JSON definition in a file that will be used by the Invoke-AzDataFactoryPipeline command. 
$pipelineParameters | Out-File c:\PipelineParameters.json

# Create a pipeline run by using parameters
$runId = Invoke-AzDataFactoryPipeline -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -PipelineName $pipelineName -ParameterFile c:\PipelineParameters.json

# Check the pipeline run status until it finishes
Start-Sleep -Seconds 30
while ($True) {
    $result = Get-AzDataFactoryActivityRun -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -PipelineRunId $runId -RunStartedAfter (Get-Date).AddMinutes(-30) -RunStartedBefore (Get-Date).AddMinutes(30)

    if (($result | Where-Object { $_.Status -eq "InProgress" } | Measure-Object).count -ne 0) {
        Write-Host "Pipeline run status: In Progress" -foregroundcolor "Yellow"
        Start-Sleep -Seconds 300
    }
    else {
        Write-Host "Pipeline $pipelineName run finished. Result:" -foregroundcolor "Yellow"
        $result
        break
    }
}

# Get the activity run details 
$result = Get-AzDataFactoryActivityRun -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName `
    -PipelineRunId $runId `
    -RunStartedAfter (Get-Date).AddMinutes(-30) `
    -RunStartedBefore (Get-Date).AddMinutes(30) `
    -ErrorAction Stop

$result

if ($result.Status -eq "Succeeded") {`
    $result.Output -join "`r`n"`
}`
else {`
    $result.Error -join "`r`n"`
}

# To remove the data factory from the resource gorup
# Remove-AzDataFactory -Name $dataFactoryName -ResourceGroupName $resourceGroupName
# 
# To remove the whole resource group
# Remove-AzResourceGroup  -Name $resourceGroupName

Clean up deployment

After you run the sample script, you can use the following command to remove the resource group and all resources associated with it:

Remove-AzResourceGroup -ResourceGroupName $resourceGroupName

To remove the data factory from the resource group, run the following command:

Remove-AzDataFactoryV2 -Name $dataFactoryName -ResourceGroupName $resourceGroupName

Script explanation

This script uses the following commands:

Command Notes
New-AzResourceGroup Creates a resource group in which all resources are stored.
Set-AzDataFactoryV2 Create a data factory.
Set-AzDataFactoryV2LinkedService Creates a linked service in the data factory. A linked service links a data store or compute to a data factory.
Set-AzDataFactoryV2Pipeline Creates a pipeline in the data factory. A pipeline contains one or more activities that performs a certain operation. In this pipeline, a spark activity transforms data by running a program on an Azure HDInsight Spark cluster.
Invoke-AzDataFactoryV2Pipeline Creates a run for the pipeline. In other words, runs the pipeline.
Get-AzDataFactoryV2ActivityRun Gets details about the run of the activity (activity run) in the pipeline.
Remove-AzResourceGroup Deletes a resource group including all nested resources.

Related content

For more information on the Azure PowerShell, see Azure PowerShell documentation.

Additional Azure Data Factory PowerShell script samples can be found in the Azure Data Factory PowerShell samples.