Quickstart: Create an Azure Data Factory using Azure CLI

Članak
05/15/2024

This quickstart describes how to use Azure CLI 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 information on how to transform data using Azure Data Factory, see Transform data in Azure Data Factory.

For an introduction to the Azure Data Factory service, see Introduction to Azure Data Factory.

If you don't have an Azure subscription, create a free account before you begin.

Prerequisites

Use the Bash environment in Azure Cloud Shell. For more information, see Quickstart for Bash in Azure Cloud Shell.
If you prefer to run CLI reference commands locally, install the Azure CLI. If you're running on Windows or macOS, consider running Azure CLI in a Docker container. For more information, see How to run the Azure CLI in a Docker container.
- If you're using a local installation, sign in to the Azure CLI by using the az login command. To finish the authentication process, follow the steps displayed in your terminal. For other sign-in options, see Sign in with the Azure CLI.
- When you're prompted, install the Azure CLI extension on first use. For more information about extensions, see Use extensions with the Azure CLI.
- Run az version to find the version and dependent libraries that are installed. To upgrade to the latest version, run az upgrade.

Note

To create Data Factory instances, the user account that you use to sign in to Azure must be a member of the contributor or owner role, or an administrator of the Azure subscription. For more information, see Azure roles.

Prepare a container and test file

This quickstart uses an Azure Storage account, which includes a container with a file.

To create a resource group named ADFQuickStartRG, use the az group create command:
```
az group create --name ADFQuickStartRG --location eastus
```

Create a storage account by using the az storage account create command:

az storage account create --resource-group ADFQuickStartRG \
    --name adfquickstartstorage --location eastus

Create a container named adftutorial by using the az storage container create command:

az storage container create --resource-group ADFQuickStartRG --name adftutorial \
    --account-name adfquickstartstorage --auth-mode key

In the local directory, create a file named emp.txt to upload. If you're working in Azure Cloud Shell, you can find the current working directory by using the echo $PWD Bash command. You can use standard Bash commands, like cat, to create a file:
```
cat > emp.txt
This is text.
```
Use Ctrl+D to save your new file.

To upload the new file to your Azure storage container, use the az storage blob upload command:

az storage blob upload --account-name adfquickstartstorage --name input/emp.txt \
    --container-name adftutorial --file emp.txt --auth-mode key

This command uploads to a new folder named input.

Create a data factory

To create an Azure data factory, run the az datafactory create command:

az datafactory create --resource-group ADFQuickStartRG \
    --factory-name ADFTutorialFactory

Important

Replace ADFTutorialFactory with a globally unique data factory name, for example, ADFTutorialFactorySP1127.

You can see the data factory that you created by using the az datafactory show command:

az datafactory show --resource-group ADFQuickStartRG \
    --factory-name ADFTutorialFactory

Create a linked service and datasets

Next, create a linked service and two datasets.

Get the connection string for your storage account by using the az storage account show-connection-string command:

az storage account show-connection-string --resource-group ADFQuickStartRG \
    --name adfquickstartstorage --key primary

In your working directory, create a JSON file with this content, which includes your own connection string from the previous step. Name the file AzureStorageLinkedService.json:

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

Create a linked service, named AzureStorageLinkedService, by using the az datafactory linked-service create command:

az datafactory linked-service create --resource-group ADFQuickStartRG \
    --factory-name ADFTutorialFactory --linked-service-name AzureStorageLinkedService \
    --properties AzureStorageLinkedService.json

In your working directory, create a JSON file with this content, named InputDataset.json:

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

Create an input dataset named InputDataset by using the az datafactory dataset create command:

az datafactory dataset create --resource-group ADFQuickStartRG \
    --dataset-name InputDataset --factory-name ADFTutorialFactory \
    --properties InputDataset.json

In your working directory, create a JSON file with this content, named OutputDataset.json:

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

Create an output dataset named OutputDataset by using the az datafactory dataset create command:

az datafactory dataset create --resource-group ADFQuickStartRG \
    --dataset-name OutputDataset --factory-name ADFTutorialFactory \
    --properties OutputDataset.json

Create and run the pipeline

Finally, create and run the pipeline.

In your working directory, create a JSON file with this content named Adfv2QuickStartPipeline.json:

{
    "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": []
    }
}

Create a pipeline named Adfv2QuickStartPipeline by using the az datafactory pipeline create command:

az datafactory pipeline create --resource-group ADFQuickStartRG \
    --factory-name ADFTutorialFactory --name Adfv2QuickStartPipeline \
    --pipeline Adfv2QuickStartPipeline.json

Run the pipeline by using the az datafactory pipeline create-run command:

az datafactory pipeline create-run --resource-group ADFQuickStartRG \
    --name Adfv2QuickStartPipeline --factory-name ADFTutorialFactory

This command returns a run ID. Copy it for use in the next command.

Verify that the pipeline run succeeded by using the az datafactory pipeline-run show command:

az datafactory pipeline-run show --resource-group ADFQuickStartRG \
    --factory-name ADFTutorialFactory --run-id 00000000-0000-0000-0000-000000000000

You can also verify that your pipeline ran as expected by using the Azure portal. For more information, see Review deployed resources.

Clean up resources

All of the resources in this quickstart are part of the same resource group. To remove them all, use the az group delete command:

az group delete --name ADFQuickStartRG

If you're using this resource group for anything else, instead, delete individual resources. For instance, to remove the linked service, use the az datafactory linked-service delete command.

In this quickstart, you created the following JSON files:

AzureStorageLinkedService.json
InputDataset.json
OutputDataset.json
Adfv2QuickStartPipeline.json

Delete them by using standard Bash commands.

Dijelite putem

Quickstart: Create an Azure Data Factory using Azure CLI

Prerequisites

Prepare a container and test file

Create a data factory

Create a linked service and datasets

Create and run the pipeline

Clean up resources

Povratne informacije

Dodatni resursi

Dijelite putem

Quickstart: Create an Azure Data Factory using Azure CLI

Prerequisites

Prepare a container and test file

Create a data factory

Create a linked service and datasets

Create and run the pipeline

Clean up resources

Related content

Povratne informacije

Dodatni resursi