Copy data from Spark using Azure Data Factory or Synapse Analytics

APPLIES TO: Azure Data Factory Azure Synapse Analytics

This article outlines how to use the Copy Activity in an Azure Data Factory or Synapse Analytics pipeline to copy data from Spark. It builds on the copy activity overview article that presents a general overview of copy activity.

This Spark connector is supported for the following capabilities:

① Azure integration runtime ② Self-hosted integration runtime

For a list of data stores that are supported as sources/sinks by the copy activity, see the Supported data stores table.

The service provides a built-in driver to enable connectivity, therefore you don't need to manually install any driver using this connector.

If your data store is located inside an on-premises network, an Azure virtual network, or Amazon Virtual Private Cloud, you need to configure a self-hosted integration runtime to connect to it.

If your data store is a managed cloud data service, you can use the Azure Integration Runtime. If the access is restricted to IPs that are approved in the firewall rules, you can add Azure Integration Runtime IPs to the allow list.

You can also use the managed virtual network integration runtime feature in Azure Data Factory to access the on-premises network without installing and configuring a self-hosted integration runtime.

For more information about the network security mechanisms and options supported by Data Factory, see Data access strategies.

To perform the Copy activity with a pipeline, you can use one of the following tools or SDKs:

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

Use the following steps to create a linked service to Spark in the Azure portal UI.

1. Browse to the Manage tab in your Azure Data Factory or Synapse workspace and select Linked Services, then click New:

   • Azure Data Factory
   • Azure Synapse

   

2. Search for Spark and select the Spark connector.

   

3. Configure the service details, test the connection, and create the new linked service.

   

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

The Spark connector now supports version 2.0 (Preview). Refer to this section to upgrade your Spark connector version from version 1.0. For the property details, see the corresponding sections.

• Version 2.0 (Preview)
• Version 1.0

The following properties are supported for Spark linked service version 2.0 (Preview):

Example:

{
    "name": "SparkLinkedService",
    "properties": {
        "type": "Spark",
        "version": "2.0",
        "typeProperties": {
            "host": "<cluster>.azurehdinsight.cn",
            "port": "<port>",
            "authenticationType": "WindowsAzureHDInsightService",
            "username": "<username>",
            "password": {
                "type": "SecureString",
                "value": "<password>"
            }
        }
    }
}

The following properties are supported for Spark linked service version 1.0:

Example:

{
    "name": "SparkLinkedService",
    "properties": {
        "type": "Spark",
        "typeProperties": {
            "host" : "<cluster>.azurehdinsight.cn",
            "port": "<port>",
            "authenticationType": "WindowsAzureHDInsightService",
            "username": "<username>",
            "password": {
                "type": "SecureString",
                "value": "<password>"
            }
        }
    }
}

For a full list of sections and properties available for defining datasets, see the datasets article. This section provides a list of properties supported by Spark dataset.

To copy data from Spark, set the type property of the dataset to SparkObject. The following properties are supported:

Example

{
    "name": "SparkDataset",
    "properties": {
        "type": "SparkObject",
        "typeProperties": {},
        "schema": [],
        "linkedServiceName": {
            "referenceName": "<Spark linked service name>",
            "type": "LinkedServiceReference"
        }
    }
}

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 Spark source.

To copy data from Spark, set the source type in the copy activity to SparkSource. The following properties are supported in the copy activity source section:

Example:

"activities":[
    {
        "name": "CopyFromSpark",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<Spark input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "SparkSource",
                "query": "SELECT * FROM MyTable"
            },
            "sink": {
                "type": "<sink type>"
            }
        }
    }
]

When you copy data from and to Spark, the following interim data type mappings are used within the service. To learn about how the copy activity maps the source schema and data type to the sink, see Schema and data type mappings.

To learn details about the properties, check Lookup activity.

1. In�Edit linked service�page, select�2.0�for version and configure the linked service by referring to�Linked service properties version 2.0 (Preview).

2. The data type mapping for the Spark linked service version 2.0 (Preview) is different from that for the version 1.0. To learn the latest data type mapping, see�Data type mapping for Spark.

The Spark connector version 2.0 (Preview) offers new functionalities and is compatible with most features of version 1.0. The following table shows the feature differences between version 2.0 (Preview) and version 1.0.

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