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title description author ms.subservice ms.devlang ms.topic ms.date ms.author ms.custom
Create Azure Data Factory using .NET SDK
Create an Azure Data Factory and pipeline using .NET SDK to copy data from one location in Azure Blob storage to another location.
jianleishen
data-movement
csharp
quickstart
12/15/2023
binluwang
mode-api, devx-track-dotnet

Quickstart: Create a data factory and pipeline using .NET SDK

[!INCLUDEappliesto-adf-xxx-md]

This quickstart describes how to use .NET SDK 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]

Visual Studio

The walkthrough in this article uses Visual Studio 2019. The procedures for Visual Studio 2013, 2015, or 2017 differ slightly.

Create an application in Microsoft Entra ID

From the sections in How to: Use the portal to create a Microsoft Entra application and service principal that can access resources, follow the instructions to do these tasks:

  1. In Create a Microsoft Entra application, create an application that represents the .NET application you're creating in this tutorial. For the sign-on URL, you can provide a dummy URL as shown in the article (https://contoso.org/exampleapp).
  2. In Get values for signing in, get the application ID and tenant ID, and note down these values that you use later in this tutorial.
  3. In Certificates and secrets, get the authentication key, and note down this value that you use later in this tutorial.
  4. In Assign the application to a role, assign the application to the Contributor role at the subscription level so that the application can create data factories in the subscription.

Create a Visual Studio project

Next, create a C# .NET console application in Visual Studio:

  1. Launch Visual Studio.
  2. In the Start window, select Create a new project > Console App (.NET Framework). .NET version 4.5.2 or above is required.
  3. In Project name, enter ADFv2QuickStart.
  4. Select Create to create the project.

Install NuGet packages

  1. Select Tools > NuGet Package Manager > Package Manager Console.

  2. In the Package Manager Console pane, run the following commands to install packages. For more information, see the Azure.ResourceManager.DataFactory NuGet package.

    Install-Package Azure.ResourceManager.DataFactory -IncludePrerelease
Install-Package Azure.Identity

Create a data factory

  1. Open Program.cs, include the following statements to add references to namespaces.

    using Azure;
using Azure.Core;
using Azure.Core.Expressions.DataFactory;
using Azure.Identity;
using Azure.ResourceManager;
using Azure.ResourceManager.DataFactory;
using Azure.ResourceManager.DataFactory.Models;
using Azure.ResourceManager.Resources;
using System;
using System.Collections.Generic;

  2. Add the following code to the Main method that sets the variables. Replace the placeholders with your own values. 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, and more) and computes (HDInsight and others) used by data factory can be in other regions.

    // Set variables
string tenantID = "<your tenant ID>";
string applicationId = "<your application ID>";
string authenticationKey = "<your authentication key for the application>";
string subscriptionId = "<your subscription ID where the data factory resides>";
string resourceGroup = "<your resource group where the data factory resides>";
string region = "<the location of your resource group>";
string dataFactoryName = 
    "<specify the name of data factory to create. It must be globally unique.>";
string storageAccountName = "<your storage account name to copy data>";
string storageKey = "<your storage account key>";
// specify the container and input folder from which all files 
// need to be copied to the output folder. 
string inputBlobContainer = "<blob container to copy data from, e.g. containername>";
string inputBlobPath = "<path to existing blob(s) to copy data from, e.g. inputdir/file>";
//specify the contains and output folder where the files are copied
string outputBlobContainer = "<blob container to copy data from, e.g. containername>";
string outputBlobPath = "<the blob path to copy data to, e.g. outputdir/file>";

// name of the Azure Storage linked service, blob dataset, and the pipeline
string storageLinkedServiceName = "AzureStorageLinkedService";
string blobDatasetName = "BlobDataset";
string pipelineName = "Adfv2QuickStartPipeline";

  3. Add the following code to the Main method that creates a data factory.

    ArmClient armClient = new ArmClient(
    new ClientSecretCredential(tenantID, applicationId, authenticationKey, new TokenCredentialOptions
    {
        AuthorityHost = AzureAuthorityHosts.AzurePublicCloud
    }), 
    subscriptionId, 
    new ArmClientOptions { Environment = ArmEnvironment.AzurePublicCloud }
);

ResourceIdentifier resourceIdentifier = SubscriptionResource.CreateResourceIdentifier(subscriptionId);
SubscriptionResource subscriptionResource = armClient.GetSubscriptionResource(resourceIdentifier);

Console.WriteLine("Get an existing resource group " + resourceGroupName + "...");
var resourceGroupOperation = subscriptionResource.GetResourceGroups().Get(resourceGroupName);
ResourceGroupResource resourceGroupResource = resourceGroupOperation.Value;

Console.WriteLine("Create a data factory " + dataFactoryName + "...");
DataFactoryData dataFactoryData = new DataFactoryData(AzureLocation.EastUS2);
var dataFactoryOperation = resourceGroupResource.GetDataFactories().CreateOrUpdate(WaitUntil.Completed, dataFactoryName, dataFactoryData);
Console.WriteLine(dataFactoryOperation.WaitForCompletionResponse().Content);

// Get the data factory resource
DataFactoryResource dataFactoryResource = dataFactoryOperation.Value;

Create a linked service

Add the following code to the Main method that creates an Azure Storage linked service.

You create linked services in a data factory to link your data stores and compute services to the data factory. In this Quickstart, you only need to create one Azure Blob Storage linked service for both the copy source and the sink store; it's named "AzureBlobStorageLinkedService" in the sample.

// Create an Azure Storage linked service
Console.WriteLine("Create a linked service " + storageLinkedServiceName + "...");
AzureBlobStorageLinkedService azureBlobStorage = new AzureBlobStorageLinkedService()
{
    ConnectionString = azureBlobStorageConnectionString
};

DataFactoryLinkedServiceData linkedServiceData = new DataFactoryLinkedServiceData(azureBlobStorage);

var linkedServiceOperation = dataFactoryResource.GetDataFactoryLinkedServices().CreateOrUpdate(WaitUntil.Completed, storageLinkedServiceName, linkedServiceData);
Console.WriteLine(linkedServiceOperation.WaitForCompletionResponse().Content);

Create a dataset

Add the following code to the Main method that creates an Delimited text dataset.

You define a dataset that represents the data to copy from a source to a sink. In this example, this Delimited text dataset references to the Azure Blob Storage linked service you created in the previous step. The dataset takes two parameters whose value is set in an activity that consumes the dataset. The parameters are used to construct the "container" and the "folderPath" pointing to where the data resides/is stored.

// Create an Azure Blob dataset
DataFactoryLinkedServiceReference linkedServiceReference = new DataFactoryLinkedServiceReference(DataFactoryLinkedServiceReferenceType.LinkedServiceReference, storageLinkedServiceName);
DelimitedTextDataset delimitedTextDataset = new DelimitedTextDataset(linkedServiceReference)
{
    DataLocation = new AzureBlobStorageLocation
    {
        Container = DataFactoryElement<string>.FromExpression("@dataset().container"),
        FileName = DataFactoryElement<string>.FromExpression("@dataset().path")
    },
    Parameters =
    {
        new KeyValuePair<string, EntityParameterSpecification>("container",new EntityParameterSpecification(EntityParameterType.String)),
        new KeyValuePair<string, EntityParameterSpecification>("path",new EntityParameterSpecification(EntityParameterType.String))
    },
    FirstRowAsHeader = false,
    QuoteChar = "\"",
    EscapeChar = "\\",
    ColumnDelimiter = ","
};

DataFactoryDatasetData datasetData = new DataFactoryDatasetData(delimitedTextDataset);

var datasetOperation = dataFactoryResource.GetDataFactoryDatasets().CreateOrUpdate(WaitUntil.Completed, blobDatasetName, datasetData);
Console.WriteLine(datasetOperation.WaitForCompletionResponse().Content);

Create a pipeline

Add the following code to the Main method that creates a pipeline with a copy activity.

In this example, this pipeline contains one activity and takes four parameters: the input blob container and path, and the output blob container and path. The values for these parameters are set when the pipeline is triggered/run. The copy activity refers to the same blob dataset created in the previous step as input and output. When the dataset is used as an input dataset, input container and path are specified. And, when the dataset is used as an output dataset, the output container and path are specified.

// Create a pipeline with a copy activity
Console.WriteLine("Creating pipeline " + pipelineName + "...");
DataFactoryPipelineData pipelineData = new DataFactoryPipelineData()
{
    Parameters =
    {
        new KeyValuePair<string, EntityParameterSpecification>("inputContainer",new EntityParameterSpecification(EntityParameterType.String)),
        new KeyValuePair<string, EntityParameterSpecification>("inputPath",new EntityParameterSpecification(EntityParameterType.String)),
        new KeyValuePair<string, EntityParameterSpecification>("outputContainer",new EntityParameterSpecification(EntityParameterType.String)),
        new KeyValuePair<string, EntityParameterSpecification>("outputPath",new EntityParameterSpecification(EntityParameterType.String))
    },
    Activities =
    {
        new CopyActivity("CopyFromBlobToBlob",new DataFactoryBlobSource(),new DataFactoryBlobSink())
        {
            Inputs =
            {
                new DatasetReference(DatasetReferenceType.DatasetReference,blobDatasetName)
                {
                    Parameters =
                    {
                        new KeyValuePair<string, BinaryData>("container", BinaryData.FromString("\"@pipeline().parameters.inputContainer\"")),
                        new KeyValuePair<string, BinaryData>("path", BinaryData.FromString("\"@pipeline().parameters.inputPath\""))
                    }
                }
            },
            Outputs =
            {
                new DatasetReference(DatasetReferenceType.DatasetReference,blobDatasetName)
                {
                    Parameters =
                    {
                        new KeyValuePair<string, BinaryData>("container", BinaryData.FromString("\"@pipeline().parameters.outputContainer\"")),
                        new KeyValuePair<string, BinaryData>("path", BinaryData.FromString("\"@pipeline().parameters.outputPath\""))
                    }
                }
            }
        }
    }
};

var pipelineOperation = dataFactoryResource.GetDataFactoryPipelines().CreateOrUpdate(WaitUntil.Completed, pipelineName, pipelineData);
Console.WriteLine(pipelineOperation.WaitForCompletionResponse().Content);

Create a pipeline run

Add the following code to the Main method that triggers a pipeline run.

This code also sets values of the inputContainer, inputPath, outputContainer, and outputPath parameters specified in the pipeline with the actual values of the source and sink blob paths.

// Create a pipeline run
Console.WriteLine("Creating pipeline run...");
Dictionary<string, BinaryData> parameters = new Dictionary<string, BinaryData>()
{
    { "inputContainer",BinaryData.FromObjectAsJson(inputBlobContainer) },
    { "inputPath",BinaryData.FromObjectAsJson(inputBlobPath) },
    { "outputContainer",BinaryData.FromObjectAsJson(outputBlobContainer) },
    { "outputPath",BinaryData.FromObjectAsJson(outputBlobPath) }
};

var pipelineResource = dataFactoryResource.GetDataFactoryPipeline(pipelineName);
var runResponse = pipelineResource.Value.CreateRun(parameters);
Console.WriteLine("Pipeline run ID: " + runResponse.Value.RunId);

Monitor a pipeline run

  1. Add the following code to the Main method to continuously check the status until it finishes copying the data.

    // Monitor the pipeline run
Console.WriteLine("Checking pipeline run status...");
DataFactoryPipelineRunInfo pipelineRun;
while (true)
{
    pipelineRun = dataFactoryResource.GetPipelineRun(runResponse.Value.RunId.ToString());
    Console.WriteLine("Status: " + pipelineRun.Status);
    if (pipelineRun.Status == "InProgress" || pipelineRun.Status == "Queued")
        System.Threading.Thread.Sleep(15000);
    else
        break;
}

  2. Add the following code to the Main method that retrieves copy activity run details, such as the size of the data that's read or written.

    // Check the copy activity run details
Console.WriteLine("Checking copy activity run details...");

var queryResponse = dataFactoryResource.GetActivityRun(pipelineRun.RunId.ToString(), 
    new RunFilterContent(DateTime.UtcNow.AddMinutes(-10), DateTime.UtcNow.AddMinutes(10)));

var enumerator = queryResponse.GetEnumerator();
enumerator.MoveNext();

if (pipelineRun.Status == "Succeeded")
    Console.WriteLine(enumerator.Current.Output);
else
    Console.WriteLine(enumerator.Current.Error);
Console.WriteLine("\nPress any key to exit...");
Console.ReadKey();

Run the code

Build and start the application, then verify the pipeline execution.

The console prints the progress of creating data factory, linked service, datasets, pipeline, and pipeline run. It then checks the pipeline run status. Wait until you see the copy activity run details with the size of the read/write data. Then use tools such as Azure Storage Explorer to check the blob(s) is copied to "outputBlobPath" from "inputBlobPath" as you specified in the variables.

Sample output

Create a data factory quickstart-adf...
{
  "name": "quickstart-adf",
  "type": "Microsoft.DataFactory/factories",
  "properties": {
    "provisioningState": "Succeeded",
    "version": "2018-06-01"
  },
  "location": "eastus2"
}
Create a linked service AzureBlobStorage...
{
  "name": "AzureBlobStorage",
  "type": "Microsoft.DataFactory/factories/linkedservices",
  "properties": {
    "type": "AzureBlobStorage",
    "typeProperties": {
      "connectionString": "DefaultEndpointsProtocol=https;AccountName=<storageAccountName>;",
      "encryptedCredential": "<encryptedCredential>"
    }
  }
}
Creating dataset BlobDelimitedDataset...
{
  "name": "BlobDelimitedDataset",
  "type": "Microsoft.DataFactory/factories/datasets",
  "properties": {
    "type": "DelimitedText",
    "linkedServiceName": {
      "type": "LinkedServiceReference",
      "referenceName": "AzureBlobStorage"
    },
    "parameters": {
      "container": {
        "type": "String"
      },
      "path": {
        "type": "String"
      }
    },
    "typeProperties": {
      "location": {
        "container": {
          "type": "Expression",
          "value": "@dataset().container"
        },
        "type": "AzureBlobStorageLocation",
        "fileName": {
          "type": "Expression",
          "value": "@dataset().path"
        }
      },
      "columnDelimiter": ",",
      "quoteChar": "\"",
      "escapeChar": "\\",
      "firstRowAsHeader": false
    }
  }
}
Creating pipeline Adfv2QuickStartPipeline...
{
  "properties": {
    "activities": [
      {
        "inputs": [
          {
            "type": "DatasetReference",
            "referenceName": "BlobDelimitedDataset",
            "parameters": {
              "container": "@pipeline().parameters.inputContainer",
              "path": "@pipeline().parameters.inputPath"
            }
          }
        ],
        "outputs": [
          {
            "type": "DatasetReference",
            "referenceName": "BlobDelimitedDataset",
            "parameters": {
              "container": "@pipeline().parameters.outputContainer",
              "path": "@pipeline().parameters.outputPath"
            }
          }
        ],
        "name": "CopyFromBlobToBlob",
        "type": "Copy",
        "typeProperties": {
          "source": {
            "type": "BlobSource"
          },
          "sink": {
            "type": "BlobSink"
          }
        }
      }
    ],
    "parameters": {
      "inputContainer": {
        "type": "String"
      },
      "inputPath": {
        "type": "String"
      },
      "outputContainer": {
        "type": "String"
      },
      "outputPath": {
        "type": "String"
      }
    }
  }
}
Creating pipeline run...
Pipeline run ID: 3aa26ffc-5bee-4db9-8bac-ccbc2d7b51c1
Checking pipeline run status...
Status: InProgress
Status: Succeeded
Checking copy activity run details...
{
  "dataRead": 1048,
  "dataWritten": 1048,
  "filesRead": 1,
  "filesWritten": 1,
  "sourcePeakConnections": 1,
  "sinkPeakConnections": 1,
  "copyDuration": 8,
  "throughput": 1.048,
  "errors": [],
  "effectiveIntegrationRuntime": "AutoResolveIntegrationRuntime (East US 2)",
  "usedDataIntegrationUnits": 4,
  "billingReference": {
    "activityType": "DataMovement",
    "billableDuration": [
      {
        "meterType": "AzureIR",
        "duration": 0.06666666666666667,
        "unit": "DIUHours"
      }
    ],
    "totalBillableDuration": [
      {
        "meterType": "AzureIR",
        "duration": 0.06666666666666667,
        "unit": "DIUHours"
      }
    ]
  },
  "usedParallelCopies": 1,
  "executionDetails": [
    {
      "source": {
        "type": "AzureBlobStorage"
      },
      "sink": {
        "type": "AzureBlobStorage"
      },
      "status": "Succeeded",
      "start": "2023-12-15T10:25:33.9991558Z",
      "duration": 8,
      "usedDataIntegrationUnits": 4,
      "usedParallelCopies": 1,
      "profile": {
        "queue": {
          "status": "Completed",
          "duration": 5
        },
        "transfer": {
          "status": "Completed",
          "duration": 1,
          "details": {
            "listingSource": {
              "type": "AzureBlobStorage",
              "workingDuration": 0
            },
            "readingFromSource": {
              "type": "AzureBlobStorage",
              "workingDuration": 0
            },
            "writingToSink": {
              "type": "AzureBlobStorage",
              "workingDuration": 0
            }
          }
        }
      },
      "detailedDurations": {
        "queuingDuration": 5,
        "transferDuration": 1
      }
    }
  ],
  "dataConsistencyVerification": {
    "VerificationResult": "NotVerified"
  }
}

Press any key to exit...

Verify the output

The pipeline automatically creates the output folder in the adftutorial blob container. Then, it copies the emp.txt file from the input folder to the output folder.

  1. In the Azure portal, on the adftutorial container page that you stopped at in the Add an input folder and file for the blob container section above, select Refresh to see the output folder.
  2. In the folder list, select output.
  3. Confirm that the emp.txt is copied to the output folder.

Clean up resources

To programmatically delete the data factory, add the following lines of code to the program:

Console.WriteLine("Deleting the data factory");
dataFactoryResource.Delete(WaitUntil.Completed);

Next steps

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.