Copy and transform data in Azure Blob storage by using Azure Data Factory

• 12/12/2019
• 26 minutes to read
• • 
  • 
  • 
  • 
  • 
  • +10

This article outlines how to use Copy Activity in Azure Data Factory to copy data from and to Azure Blob storage, and use Data Flow to transform data in Azure Blob storage. To learn about Azure Data Factory, read the introductory article.

Supported capabilities

This Azure Blob connector is supported for the following activities:

• Copy activity with supported source/sink matrix
• Mapping data flow
• Lookup activity
• GetMetadata activity
• Delete activity

For Copy activity, this Blob storage connector supports:

• Copying blobs to and from general-purpose Azure storage accounts and hot/cool blob storage.
• Copying blobs by using account key, service shared access signature, service principal or managed identities for Azure resources authentications.
• Copying blobs from block, append, or page blobs and copying data to only block blobs.
• Copying blobs as is or parsing or generating blobs with supported file formats and compression codecs.
• Preserve file metadata during copy.

Get started

You can use one of the following tools or SDKs to use the copy activity with a pipeline. Select a link for step-by-step instructions:

• Copy data tool
• Azure portal
• .NET SDK
• Python SDK
• Azure PowerShell
• REST API
• Azure Resource Manager template

The following sections provide details about properties that are used to define Data Factory entities specific to Blob storage.

Linked service properties

Azure Blob connector support the following authentication types, refer to the corresponding section on details:

• Account key authentication
• Shared access signature authentication
• Service principal authentication
• Managed identities for Azure resources authentication

Account key authentication

To use storage account key authentication, the following properties are supported:

Example:

{
    "name": "AzureBlobStorageLinkedService",
    "properties": {
        "type": "AzureBlobStorage",
        "typeProperties": {
            "connectionString": {
                "type": "SecureString",
                "value": "DefaultEndpointsProtocol=https;AccountName=<accountname>;AccountKey=<accountkey>"
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Example: store account key in Azure Key Vault

{
    "name": "AzureBlobStorageLinkedService",
    "properties": {
        "type": "AzureBlobStorage",
        "typeProperties": {
            "connectionString": {
                "type": "SecureString",
                "value": "DefaultEndpointsProtocol=https;AccountName=<accountname>;"
            },
            "accountKey": { 
                "type": "AzureKeyVaultSecret", 
                "store": { 
                    "referenceName": "<Azure Key Vault linked service name>", 
                    "type": "LinkedServiceReference" 
                }, 
                "secretName": "<secretName>" 
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }            
    }
}

Shared access signature authentication

A shared access signature provides delegated access to resources in your storage account. You can use a shared access signature to grant a client limited permissions to objects in your storage account for a specified time. You don't have to share your account access keys. The shared access signature is a URI that encompasses in its query parameters all the information necessary for authenticated access to a storage resource. To access storage resources with the shared access signature, the client only needs to pass in the shared access signature to the appropriate constructor or method. For more information about shared access signatures, see Shared access signatures: Understand the shared access signature model.

To use shared access signature authentication, the following properties are supported:

Example:

{
    "name": "AzureBlobStorageLinkedService",
    "properties": {
        "type": "AzureBlobStorage",
        "typeProperties": {
            "sasUri": {
                "type": "SecureString",
                "value": "<SAS URI of the Azure Storage resource e.g. https://<container>.blob.core.windows.net/?sv=<storage version>&amp;st=<start time>&amp;se=<expire time>&amp;sr=<resource>&amp;sp=<permissions>&amp;sip=<ip range>&amp;spr=<protocol>&amp;sig=<signature>>"
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Example: store account key in Azure Key Vault

{
    "name": "AzureBlobStorageLinkedService",
    "properties": {
        "type": "AzureBlobStorage",
        "typeProperties": {
            "sasUri": {
                "type": "SecureString",
                "value": "<SAS URI of the Azure Storage resource without token e.g. https://<container>.blob.core.windows.net/>"
            },
            "sasToken": { 
                "type": "AzureKeyVaultSecret", 
                "store": { 
                    "referenceName": "<Azure Key Vault linked service name>", 
                    "type": "LinkedServiceReference" 
                }, 
                "secretName": "<secretName>" 
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

When you create a shared access signature URI, consider the following points:

• Set appropriate read/write permissions on objects based on how the linked service (read, write, read/write) is used in your data factory.
• Set Expiry time appropriately. Make sure that the access to Storage objects doesn't expire within the active period of the pipeline.
• The URI should be created at the right container/blob based on the need. A shared access signature URI to a blob allows Data Factory to access that particular blob. A shared access signature URI to a Blob storage container allows Data Factory to iterate through blobs in that container. To provide access to more or fewer objects later, or to update the shared access signature URI, remember to update the linked service with the new URI.

Service principal authentication

For Azure Storage service principal authentication in general, refer to Authenticate access to Azure Storage using Azure Active Directory.

To use service principal authentication, follow these steps:

1. Register an application entity in Azure Active Directory (Azure AD) by following Register your application with an Azure AD tenant. Make note of the following values, which you use to define the linked service:

   • Application ID
   • Application key
   • Tenant ID

2. Grant the service principal proper permission in Azure Blob storage. Refer to Manage access rights to Azure Storage data with RBAC with more details on the roles.

   • As source, in Access control (IAM), grant at least Storage Blob Data Reader role.
   • As sink, in Access control (IAM), grant at least Storage Blob Data Contributor role.

These properties are supported for an Azure Blob storage linked service:

Example:

{
    "name": "AzureBlobStorageLinkedService",
    "properties": {
        "type": "AzureBlobStorage",
        "typeProperties": {            
            "serviceEndpoint": "https://<accountName>.blob.core.windows.net/",
            "servicePrincipalId": "<service principal id>",
            "servicePrincipalKey": {
                "type": "SecureString",
                "value": "<service principal key>"
            },
            "tenant": "<tenant info, e.g. microsoft.onmicrosoft.com>" 
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Managed identities for Azure resources authentication

A data factory can be associated with a managed identity for Azure resources, which represents this specific data factory. You can directly use this managed identity for Blob storage authentication similar to using your own service principal. It allows this designated factory to access and copy data from/to your Blob storage.

Refer to Authenticate access to Azure Storage using Azure Active Directory for Azure Storage authentication in general. To use managed identities for Azure resources authentication, follow these steps:

1. Retrieve data factory managed identity information by copying the value of "SERVICE IDENTITY APPLICATION ID" generated along with your factory.

2. Grant the managed identity proper permission in Azure Blob storage. Refer to Manage access rights to Azure Storage data with RBAC with more details on the roles.

   • As source, in Access control (IAM), grant at least Storage Blob Data Reader role.
   • As sink, in Access control (IAM), grant at least Storage Blob Data Contributor role.

These properties are supported for an Azure Blob storage linked service:

Example:

{
    "name": "AzureBlobStorageLinkedService",
    "properties": {
        "type": "AzureBlobStorage",
        "typeProperties": {            
            "serviceEndpoint": "https://<accountName>.blob.core.windows.net/"
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Dataset properties

For a full list of sections and properties available for defining datasets, see the Datasets article.

Azure Data Factory support the following file formats. Refer to each article on format-based settings.

• Avro format
• Binary format
• Delimited text format
• JSON format
• ORC format
• Parquet format

The following properties are supported for Azure Blob under location settings in format-based dataset:

Example:

{
    "name": "DelimitedTextDataset",
    "properties": {
        "type": "DelimitedText",
        "linkedServiceName": {
            "referenceName": "<Azure Blob Storage linked service name>",
            "type": "LinkedServiceReference"
        },
        "schema": [ < physical schema, optional, auto retrieved during authoring > ],
        "typeProperties": {
            "location": {
                "type": "AzureBlobStorageLocation",
                "container": "containername",
                "folderPath": "folder/subfolder"
            },
            "columnDelimiter": ",",
            "quoteChar": "\"",
            "firstRowAsHeader": true,
            "compressionCodec": "gzip"
        }
    }
}

Copy activity properties

For a full list of sections and properties available for defining activities, see the Pipelines article. This section provides a list of properties supported by the Blob storage source and sink.

Blob storage as a source type

Azure Data Factory support the following file formats. Refer to each article on format-based settings.

• Avro format
• Binary format
• Delimited text format
• JSON format
• ORC format
• Parquet format

The following properties are supported for Azure Blob under storeSettings settings in format-based copy source:

Example:

"activities":[
    {
        "name": "CopyFromBlob",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<Delimited text input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "DelimitedTextSource",
                "formatSettings":{
                    "type": "DelimitedTextReadSetting",
                    "skipLineCount": 10
                },
                "storeSettings":{
                    "type": "AzureBlobStorageReadSetting",
                    "recursive": true,
                    "wildcardFolderPath": "myfolder*A",
                    "wildcardFileName": "*.csv"
                }
            },
            "sink": {
                "type": "<sink type>"
            }
        }
    }
]

Blob storage as a sink type

Azure Data Factory support the following file formats. Refer to each article on format-based settings.

• Avro format
• Binary format
• Delimited text format
• JSON format
• ORC format
• Parquet format

The following properties are supported for Azure Blob under storeSettings settings in format-based copy sink:

Example:

"activities":[
    {
        "name": "CopyFromBlob",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<Parquet output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "<source type>"
            },
            "sink": {
                "type": "ParquetSink",
                "storeSettings":{
                    "type": "AzureBlobStorageWriteSetting",
                    "copyBehavior": "PreserveHierarchy"
                }
            }
        }
    }
]

Folder and file filter examples

This section describes the resulting behavior of the folder path and file name with wildcard filters.

Some recursive and copyBehavior examples

This section describes the resulting behavior of the Copy operation for different combinations of recursive and copyBehavior values.

Preserve metadata during copy

When you copy files from Amazon S3/Azure Blob/Azure Data Lake Storage Gen2 to Azure Data Lake Storage Gen2/Azure Blob, you can choose to preserve the file metadata along with data. Learn more from Preserve metadata.

Mapping data flow properties

When transforming data in mapping data flow, you can read and write files from Azure Blob Storage in JSON, Avro, Delimited Text, or Parquet format. For more information, see source transformation and sink transformation in the mapping data flow feature.

Source transformation

In the source transformation, you can read from a container, folder or individual file in Azure Blob Storage. The Source options tab lets you manage how the files get read.

Wildcard path: Using a wildcard pattern will instruct ADF to loop through each matching folder and file in a single Source transformation. This is an effective way to process multiple files within a single flow. Add multiple wildcard matching patterns with the + sign that appears when hovering over your existing wildcard pattern.

From your source container, choose a series of files that match a pattern. Only container can be specified in the dataset. Your wildcard path must therefore also include your folder path from the root folder.

Wildcard examples:

• * Represents any set of characters

• ** Represents recursive directory nesting

• ? Replaces one character

• [] Matches one of more characters in the brackets

• /data/sales/**/*.csv Gets all csv files under /data/sales

• /data/sales/20??/** Gets all files in the 20th century

• /data/sales/2004/*/12/[XY]1?.csv Gets all csv files in 2004 in December starting with X or Y prefixed by a two-digit number

Partition Root Path: If you have partitioned folders in your file source with a key=value format (for example, year=2019), then you can assign the top level of that partition folder tree to a column name in your data flow data stream.

First, set a wildcard to include all paths that are the partitioned folders plus the leaf files that you wish to read.

Use the Partition Root Path setting to define what the top level of the folder structure is. When you view the contents of your data via a data preview, you'll see that ADF will add the resolved partitions found in each of your folder levels.

List of files: This is a file set. Create a text file that includes a list of relative path files to process. Point to this text file.

Column to store file name: Store the name of the source file in a column in your data. Enter a new column name here to store the file name string.

After completion: Choose to do nothing with the source file after the data flow runs, delete the source file, or move the source file. The paths for the move are relative.

To move source files to another location post-processing, first select "Move" for file operation. Then, set the "from" directory. If you're not using any wildcards for your path, then the "from" setting will be the same folder as your source folder.

If you have a source path with wildcard, your syntax will look like this below:

/data/sales/20??/**/*.csv

You can specify "from" as

/data/sales

And "to" as

/backup/priorSales

In this case, all files that were sourced under /data/sales are moved to /backup/priorSales.

Filter by last modified: You can filter which files you process by specifying a date range of when they were last modified. All date-times are in UTC.

Sink properties

In the sink transformation, you can write to either a container or folder in Azure Blob Storage. the Settings tab lets you manage how the files get written.

Clear the folder: Determines whether or not the destination folder gets cleared before the data is written.

File name option: Determines how the destination files are named in the destination folder. The file name options are:

• Default: Allow Spark to name files based on PART defaults.
• Pattern: Enter a pattern that enumerates your output files per partition. For example, loans[n].csv will create loans1.csv, loans2.csv, and so on.
• Per partition: Enter one file name per partition.
• As data in column: Set the output file to the value of a column. The path is relative to the dataset container, not the destination folder.
• Output to a single file: Combine the partitioned output files into a single named file. The path is relative to the dataset folder. Please be aware that te merge operation can possibly fail based upon node size. This option is not recommended for large datasets.

Quote all: Determines whether to enclose all values in quotes

Lookup activity properties

To learn details about the properties, check Lookup activity.

GetMetadata activity properties

To learn details about the properties, check GetMetadata activity

Delete activity properties

To learn details about the properties, check Delete activity

Legacy models

Legacy dataset model

Example:

{
    "name": "AzureBlobDataset",
    "properties": {
        "type": "AzureBlob",
        "linkedServiceName": {
            "referenceName": "<Azure Blob storage linked service name>",
            "type": "LinkedServiceReference"
        },
        "typeProperties": {
            "folderPath": "mycontainer/myfolder",
            "fileName": "*",
            "modifiedDatetimeStart": "2018-12-01T05:00:00Z",
            "modifiedDatetimeEnd": "2018-12-01T06:00:00Z",
            "format": {
                "type": "TextFormat",
                "columnDelimiter": ",",
                "rowDelimiter": "\n"
            },
            "compression": {
                "type": "GZip",
                "level": "Optimal"
            }
        }
    }
}

Legacy copy activity source model

Example:

"activities":[
    {
        "name": "CopyFromBlob",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<Azure Blob input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "BlobSource",
                "recursive": true
            },
            "sink": {
                "type": "<sink type>"
            }
        }
    }
]

Legacy copy activity sink model

Example:

"activities":[
    {
        "name": "CopyToBlob",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<Azure Blob output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "<source type>"
            },
            "sink": {
                "type": "BlobSink",
                "copyBehavior": "PreserveHierarchy"
            }
        }
    }
]

Next steps

For a list of data stores supported as sources and sinks by the copy activity in Data Factory, see Supported data stores.