Copy and transform data to and from SQL Server by using Azure Data Factory or Azure Synapse Analytics

APPLIES TO: Azure Data Factory Azure Synapse Analytics

This article outlines how to use the copy activity in Azure Data Factory and Azure Synapse pipelines to copy data from and to SQL Server database and use Data Flow to transform data in SQL Server database. To learn more read the introductory article for Azure Data Factory or Azure Synapse Analytics.

Supported capabilities

This SQL Server 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 or sinks by the copy activity, see the Supported data stores table.

Specifically, this SQL Server connector supports:

• SQL Server version 2005 and above.
• Copying data by using SQL or Windows authentication.
• As a source, retrieving data by using a SQL query or a stored procedure. You can also choose to parallel copy from SQL Server source, see the Parallel copy from SQL database section for details.
• As a sink, automatically creating destination table if not exists based on the source schema; appending data to a table or invoking a stored procedure with custom logic during copy.

SQL Server Express LocalDB is not supported.

Prerequisites

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.

Get started

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

Create a SQL Server linked service using UI

Use the following steps to create a SQL Server linked service 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 SQL and select the SQL Server connector.

   

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

   

Connector configuration details

The following sections provide details about properties that are used to define Data Factory and Synapse pipeline entities specific to the SQL Server database connector.

Linked service properties

The SQL Server Recommended version supports TLS 1.3. Refer to this section to upgrade your SQL Server linked service if you use Legacy version. For the property details, see the corresponding sections.

• Recommended version
• Legacy version

Recommended version

These generic properties are supported for a SQL server linked service when you apply Recommended version:

For additional connection properties, see the table below:

SQL authentication

To use SQL authentication, in addition to the generic properties that are described in the preceding section, specify the following properties:

Example: Use SQL authentication

{
    "name": "SqlServerLinkedService",
    "properties": {
        "type": "SqlServer",
        "typeProperties": {
            "server": "<name or network address of the SQL server instance>",
            "database": "<database name>",
            "encrypt": "<encrypt>",
            "trustServerCertificate": false,
            "authenticationType": "SQL",
            "userName": "<user name>",
            "password": {
                "type": "SecureString",
                "value": "<password>"
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Example: Use SQL authentication with a password in Azure Key Vault

{
    "name": "SqlServerLinkedService",
    "properties": {
        "type": "SqlServer",
        "typeProperties": {
            "server": "<name or network address of the SQL server instance>",
            "database": "<database name>",
            "encrypt": "<encrypt>",
            "trustServerCertificate": false,
            "authenticationType": "SQL",
            "userName": "<user name>",
            "password": { 
                "type": "AzureKeyVaultSecret", 
                "store": { 
                    "referenceName": "<Azure Key Vault linked service name>", 
                    "type": "LinkedServiceReference" 
                }, 
                "secretName": "<secretName>" 
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Example: Use Always Encrypted

{
    "name": "SqlServerLinkedService",
    "properties": {
        "type": "SqlServer",
        "typeProperties": {
            "server": "<name or network address of the SQL server instance>",
            "database": "<database name>",
            "encrypt": "<encrypt>",
            "trustServerCertificate": false,
            "authenticationType": "SQL",
            "userName": "<user name>",
            "password": {
                "type": "SecureString",
                "value": "<password>"
            }
        },
        "alwaysEncryptedSettings": {
            "alwaysEncryptedAkvAuthType": "ServicePrincipal",
            "servicePrincipalId": "<service principal id>",
            "servicePrincipalKey": {
                "type": "SecureString",
                "value": "<service principal key>"
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Windows authentication

To use Windows authentication, in addition to the generic properties that are described in the preceding section, specify the following properties:

Example: Use Windows authentication

{
    "name": "SqlServerLinkedService",
    "properties": {
        "type": "SqlServer",
        "typeProperties": {
            "server": "<name or network address of the SQL server instance>",
            "database": "<database name>",
            "encrypt": "<encrypt>",
            "trustServerCertificate": false,
            "authenticationType": "Windows",
            "userName": "<domain\\username>",
            "password": {
                "type": "SecureString",
                "value": "<password>"
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Example: Use Windows authentication with a password in Azure Key Vault

{
    "name": "SqlServerLinkedService",
    "properties": {
        "annotations": [],
        "type": "SqlServer",
        "typeProperties": {
            "server": "<name or network address of the SQL server instance>",
            "database": "<database name>",
            "encrypt": "<encrypt>",
            "trustServerCertificate": false,
            "authenticationType": "Windows",
            "userName": "<domain\\username>",
            "password": {
                "type": "AzureKeyVaultSecret",
                "store": {
                    "referenceName": "<Azure Key Vault linked service name>",
                    "type": "LinkedServiceReference"
                },
                "secretName": "<secretName>"
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Legacy version

These generic properties are supported for a SQL server linked service when you apply Legacy version:

This SQL server connector supports the following authentication types. See the corresponding sections for details.

• SQL authentication for the legacy version
• Windows authentication for the legacy version

SQL authentication for the legacy version

To use SQL authentication, in addition to the generic properties that are described in the preceding section, specify the following properties:

Windows authentication for the legacy version

To use Windows authentication, in addition to the generic properties that are described in the preceding section, specify the following properties:

Dataset properties

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 the SQL Server dataset.

To copy data from and to a SQL Server database, the following properties are supported:

Example

{
    "name": "SQLServerDataset",
    "properties":
    {
        "type": "SqlServerTable",
        "linkedServiceName": {
            "referenceName": "<SQL Server linked service name>",
            "type": "LinkedServiceReference"
        },
        "schema": [ < physical schema, optional, retrievable during authoring > ],
        "typeProperties": {
            "schema": "<schema_name>",
            "table": "<table_name>"
        }
    }
}

Copy activity properties

For a full list of sections and properties available for use to define activities, see the Pipelines article. This section provides a list of properties supported by the SQL Server source and sink.

SQL Server as a source

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

Note the following points:

• If sqlReaderQuery is specified for SqlSource, the copy activity runs this query against the SQL Server source to get the data. You also can specify a stored procedure by specifying sqlReaderStoredProcedureName and storedProcedureParameters if the stored procedure takes parameters.
• When using stored procedure in source to retrieve data, note if your stored procedure is designed as returning different schema when different parameter value is passed in, you may encounter failure or see unexpected result when importing schema from UI or when copying data to SQL database with auto table creation.

Example: Use SQL query

"activities":[
    {
        "name": "CopyFromSQLServer",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<SQL Server input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "SqlSource",
                "sqlReaderQuery": "SELECT * FROM MyTable"
            },
            "sink": {
                "type": "<sink type>"
            }
        }
    }
]

Example: Use a stored procedure

"activities":[
    {
        "name": "CopyFromSQLServer",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<SQL Server input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "SqlSource",
                "sqlReaderStoredProcedureName": "CopyTestSrcStoredProcedureWithParameters",
                "storedProcedureParameters": {
                    "stringData": { "value": "str3" },
                    "identifier": { "value": "$$Text.Format('{0:yyyy}', <datetime parameter>)", "type": "Int"}
                }
            },
            "sink": {
                "type": "<sink type>"
            }
        }
    }
]

The stored procedure definition

CREATE PROCEDURE CopyTestSrcStoredProcedureWithParameters
(
    @stringData varchar(20),
    @identifier int
)
AS
SET NOCOUNT ON;
BEGIN
    select *
    from dbo.UnitTestSrcTable
    where dbo.UnitTestSrcTable.stringData != stringData
    and dbo.UnitTestSrcTable.identifier != identifier
END
GO

SQL Server as a sink

To copy data to SQL Server, set the sink type in the copy activity to SqlSink. The following properties are supported in the copy activity sink section:

Example 1: Append data

"activities":[
    {
        "name": "CopyToSQLServer",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<SQL Server output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "<source type>"
            },
            "sink": {
                "type": "SqlSink",
                "tableOption": "autoCreate",
                "writeBatchSize": 100000
            }
        }
    }
]

Example 2: Invoke a stored procedure during copy

Learn more details from Invoke a stored procedure from a SQL sink.

"activities":[
    {
        "name": "CopyToSQLServer",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<SQL Server output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "<source type>"
            },
            "sink": {
                "type": "SqlSink",
                "sqlWriterStoredProcedureName": "CopyTestStoredProcedureWithParameters",
                "storedProcedureTableTypeParameterName": "MyTable",
                "sqlWriterTableType": "MyTableType",
                "storedProcedureParameters": {
                    "identifier": { "value": "1", "type": "Int" },
                    "stringData": { "value": "str1" }
                }
            }
        }
    }
]

Example 3: Upsert data

"activities":[
    {
        "name": "CopyToSQLServer",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<SQL Server output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "<source type>"
            },
            "sink": {
                "type": "SqlSink",
                "tableOption": "autoCreate",
                "writeBehavior": "upsert",
                "upsertSettings": {
                    "useTempDB": true,
                    "keys": [
                        "<column name>"
                    ]
                },
            }
        }
    }
]

Parallel copy from SQL database

The SQL Server connector in copy activity provides built-in data partitioning to copy data in parallel. You can find data partitioning options on the Source tab of the copy activity.

When you enable partitioned copy, copy activity runs parallel queries against your SQL Server source to load data by partitions. The parallel degree is controlled by the parallelCopies setting on the copy activity. For example, if you set parallelCopies to four, the service concurrently generates and runs four queries based on your specified partition option and settings, and each query retrieves a portion of data from your SQL Server.

You are suggested to enable parallel copy with data partitioning especially when you load large amount of data from your SQL Server. The following are suggested configurations for different scenarios. When copying data into file-based data store, it's recommended to write to a folder as multiple files (only specify folder name), in which case the performance is better than writing to a single file.

Best practices to load data with partition option:

1. Choose distinctive column as partition column (like primary key or unique key) to avoid data skew.
2. If the table has built-in partition, use partition option "Physical partitions of table" to get better performance.
3. If you use Azure Integration Runtime to copy data, you can set larger "Data Integration Units (DIU)" (>4) to utilize more computing resource. Check the applicable scenarios there.
4. "Degree of copy parallelism" control the partition numbers, setting this number too large sometime hurts the performance, recommend setting this number as (DIU or number of Self-hosted IR nodes) * (2 to 4).

Example: full load from large table with physical partitions

"source": {
    "type": "SqlSource",
    "partitionOption": "PhysicalPartitionsOfTable"
}

Example: query with dynamic range partition

"source": {
    "type": "SqlSource",
    "query": "SELECT * FROM <TableName> WHERE ?DfDynamicRangePartitionCondition AND <your_additional_where_clause>",
    "partitionOption": "DynamicRange",
    "partitionSettings": {
        "partitionColumnName": "<partition_column_name>",
        "partitionUpperBound": "<upper_value_of_partition_column (optional) to decide the partition stride, not as data filter>",
        "partitionLowerBound": "<lower_value_of_partition_column (optional) to decide the partition stride, not as data filter>"
    }
}

Sample query to check physical partition

SELECT DISTINCT s.name AS SchemaName, t.name AS TableName, pf.name AS PartitionFunctionName, c.name AS ColumnName, iif(pf.name is null, 'no', 'yes') AS HasPartition
FROM sys.tables AS t
LEFT JOIN sys.objects AS o ON t.object_id = o.object_id
LEFT JOIN sys.schemas AS s ON o.schema_id = s.schema_id
LEFT JOIN sys.indexes AS i ON t.object_id = i.object_id 
LEFT JOIN sys.index_columns AS ic ON ic.partition_ordinal > 0 AND ic.index_id = i.index_id AND ic.object_id = t.object_id 
LEFT JOIN sys.columns AS c ON c.object_id = ic.object_id AND c.column_id = ic.column_id 
LEFT JOIN sys.partition_schemes ps ON i.data_space_id = ps.data_space_id 
LEFT JOIN sys.partition_functions pf ON pf.function_id = ps.function_id 
WHERE s.name='[your schema]' AND t.name = '[your table name]'

If the table has physical partition, you would see "HasPartition" as "yes" like the following.

Best practice for loading data into SQL Server

When you copy data into SQL Server, you might require different write behavior:

• Append: My source data has only new records.
• Upsert: My source data has both inserts and updates.
• Overwrite: I want to reload the entire dimension table each time.
• Write with custom logic: I need extra processing before the final insertion into the destination table.

See the respective sections for how to configure and best practices.

Append data

Appending data is the default behavior of this SQL Server sink connector. The service does a bulk insert to write to your table efficiently. You can configure the source and sink accordingly in the copy activity.

Upsert data

Copy activity now supports natively loading data into a database temporary table and then update the data in sink table if key exists and otherwise insert new data. To learn more about upsert settings in copy activities, see SQL Server as a sink.

Overwrite the entire table

You can configure the preCopyScript property in a copy activity sink. In this case, for each copy activity that runs, the service runs the script first. Then it runs the copy to insert the data. For example, to overwrite the entire table with the latest data, specify a script to first delete all the records before you bulk load the new data from the source.

Write data with custom logic

The steps to write data with custom logic are similar to those described in the Upsert data section. When you need to apply extra processing before the final insertion of source data into the destination table, you can load to a staging table then invoke stored procedure activity, or invoke a stored procedure in copy activity sink to apply data.

Invoke a stored procedure from a SQL sink

When you copy data into SQL Server database, you also can configure and invoke a user-specified stored procedure with additional parameters on each batch of the source table. The stored procedure feature takes advantage of table-valued parameters. Note that the service automatically wraps the stored procedure in its own transaction, so any transaction created inside the stored procedure will become a nested transaction, and could have implications for exception handling.

You can use a stored procedure when built-in copy mechanisms don't serve the purpose. An example is when you want to apply extra processing before the final insertion of source data into the destination table. Some extra processing examples are when you want to merge columns, look up additional values, and insert into more than one table.

The following sample shows how to use a stored procedure to do an upsert into a table in the SQL Server database. Assume that the input data and the sink Marketing table each have three columns: ProfileID, State, and Category. Do the upsert based on the ProfileID column, and only apply it for a specific category called "ProductA".

1. In your database, define the table type with the same name as sqlWriterTableType. The schema of the table type is the same as the schema returned by your input data.

   CREATE TYPE [dbo].[MarketingType] AS TABLE(
       [ProfileID] [varchar](256) NOT NULL,
       [State] [varchar](256) NOT NULL,
       [Category] [varchar](256) NOT NULL
   )
   

2. In your database, define the stored procedure with the same name as sqlWriterStoredProcedureName. It handles input data from your specified source and merges into the output table. The parameter name of the table type in the stored procedure is the same as tableName defined in the dataset.

   CREATE PROCEDURE spOverwriteMarketing @Marketing [dbo].[MarketingType] READONLY, @category varchar(256)
   AS
   BEGIN
   MERGE [dbo].[Marketing] AS target
   USING @Marketing AS source
   ON (target.ProfileID = source.ProfileID and target.Category = @category)
   WHEN MATCHED THEN
       UPDATE SET State = source.State
   WHEN NOT MATCHED THEN
       INSERT (ProfileID, State, Category)
       VALUES (source.ProfileID, source.State, source.Category);
   END
   

3. Define the SQL sink section in the copy activity as follows:

   "sink": {
       "type": "SqlSink",
       "sqlWriterStoredProcedureName": "spOverwriteMarketing",
       "storedProcedureTableTypeParameterName": "Marketing",
       "sqlWriterTableType": "MarketingType",
       "storedProcedureParameters": {
           "category": {
               "value": "ProductA"
           }
       }
   }
   

Mapping data flow properties

When transforming data in mapping data flow, you can read and write to tables from SQL Server Database. For more information, see the source transformation and sink transformation in mapping data flows.

Source transformation

The below table lists the properties supported by SQL Server source. You can edit these properties in the Source options tab.

CREATE PROC CTESP @query nvarchar(max)
AS
BEGIN
EXECUTE sp_executesql @query;
END

SQL Server source script example

When you use SQL Server as source type, the associated data flow script is:

source(allowSchemaDrift: true,
    validateSchema: false,
    isolationLevel: 'READ_UNCOMMITTED',
    query: 'select * from MYTABLE',
    format: 'query') ~> SQLSource

Sink transformation

The below table lists the properties supported by SQL Server sink. You can edit these properties in the Sink options tab.

SQL Server sink script example

When you use SQL Server as sink type, the associated data flow script is:

IncomingStream sink(allowSchemaDrift: true,
    validateSchema: false,
    deletable:false,
    insertable:true,
    updateable:true,
    upsertable:true,
    keys:['keyColumn'],
    format: 'table',
    skipDuplicateMapInputs: true,
    skipDuplicateMapOutputs: true) ~> SQLSink

Data type mapping for SQL Server

When you copy data from and to SQL Server, the following mappings are used from SQL Server data types to Azure Data Factory interim data types. Synapse pipelines, which implement Data Factory, use the same mappings. To learn how the copy activity maps the source schema and data type to the sink, see Schema and data type mappings.

Lookup activity properties

To learn details about the properties, check Lookup activity.

GetMetadata activity properties

To learn details about the properties, check GetMetadata activity

Using Always Encrypted

When you copy data from/to SQL Server with Always Encrypted, follow below steps:

1. Store the Column Master Key (CMK) in an Azure Key Vault. Learn more on how to configure Always Encrypted by using Azure Key Vault

2. Make sure to grant access to the key vault where the Column Master Key (CMK) is stored. Refer to this article for required permissions.

3. Create linked service to connect to your SQL database and enable 'Always Encrypted' function by using either managed identity or service principal.

Native change data capture

Azure Data Factory can support native change data capture capabilities for SQL Server, Azure SQL DB and Azure SQL MI. The changed data including row insert, update and deletion in SQL stores can be automatically detected and extracted by ADF mapping dataflow. With the no code experience in mapping dataflow, users can easily achieve data replication scenario from SQL stores by appending a database as destination store. What is more, users can also compose any data transform logic in between to achieve incremental ETL scenario from SQL stores.

Make sure you keep the pipeline and activity name unchanged, so that the checkpoint can be recorded by ADF for you to get changed data from the last run automatically. If you change your pipeline name or activity name, the checkpoint will be reset, which leads you to start from beginning or get changes from now in the next run. If you do want to change the pipeline name or activity name but still keep the checkpoint to get changed data from the last run automatically, please use your own Checkpoint key in dataflow activity to achieve that.

When you debug the pipeline, this feature works the same. Be aware that the checkpoint will be reset when you refresh your browser during the debug run. After you are satisfied with the pipeline result from debug run, you can go ahead to publish and trigger the pipeline. At the moment when you first time trigger your published pipeline, it automatically restarts from the beginning or gets changes from now on.

In the monitoring section, you always have the chance to rerun a pipeline. When you are doing so, the changed data is always captured from the previous checkpoint of your selected pipeline run.

Example 1:

When you directly chain a source transform referenced to SQL CDC enabled dataset with a sink transform referenced to a database in a mapping dataflow, the changes happened on SQL source will be automatically applied to the target database, so that you will easily get data replication scenario between databases. You can use update method in sink transform to select whether you want to allow insert, allow update or allow delete on target database. The example script in mapping dataflow is as below.

source(output(
		id as integer,
		name as string
	),
	allowSchemaDrift: true,
	validateSchema: false,
	enableNativeCdc: true,
	netChanges: true,
	skipInitialLoad: false,
	isolationLevel: 'READ_UNCOMMITTED',
	format: 'table') ~> source1
source1 sink(allowSchemaDrift: true,
	validateSchema: false,
	deletable:true,
	insertable:true,
	updateable:true,
	upsertable:true,
	keys:['id'],
	format: 'table',
	skipDuplicateMapInputs: true,
	skipDuplicateMapOutputs: true,
	errorHandlingOption: 'stopOnFirstError') ~> sink1

Example 2:

If you want to enable ETL scenario instead of data replication between database via SQL CDC, you can use expressions in mapping dataflow including isInsert(1), isUpdate(1) and isDelete(1) to differentiate the rows with different operation types. The following is one of the example scripts for mapping dataflow on deriving one column with the value: 1 to indicate inserted rows, 2 to indicate updated rows and 3 to indicate deleted rows for downstream transforms to process the delta data.

source(output(
		id as integer,
		name as string
	),
	allowSchemaDrift: true,
	validateSchema: false,
	enableNativeCdc: true,
	netChanges: true,
	skipInitialLoad: false,
	isolationLevel: 'READ_UNCOMMITTED',
	format: 'table') ~> source1
source1 derive(operationType = iif(isInsert(1), 1, iif(isUpdate(1), 2, 3))) ~> derivedColumn1
derivedColumn1 sink(allowSchemaDrift: true,
	validateSchema: false,
	skipDuplicateMapInputs: true,
	skipDuplicateMapOutputs: true) ~> sink1

Known limitation:

• Only net changes from SQL CDC will be loaded by ADF via cdc.fn_cdc_get_net_changes_.

Troubleshoot connection issues

1. Configure your SQL Server instance to accept remote connections. Start SQL Server Management Studio, right-click server, and select Properties. Select Connections from the list, and select the Allow remote connections to this server check box.

   

   For detailed steps, see Configure the remote access server configuration option.

2. Start SQL Server Configuration Manager. Expand SQL Server Network Configuration for the instance you want, and select Protocols for MSSQLSERVER. Protocols appear in the right pane. Enable TCP/IP by right-clicking TCP/IP and selecting Enable.

   

   For more information and alternate ways of enabling TCP/IP protocol, see Enable or disable a server network protocol.

3. In the same window, double-click TCP/IP to launch the TCP/IP Properties window.

4. Switch to the IP Addresses tab. Scroll down to see the IPAll section. Write down the TCP Port. The default is 1433.

5. Create a rule for the Windows Firewall on the machine to allow incoming traffic through this port.

6. Verify connection: To connect to SQL Server by using a fully qualified name, use SQL Server Management Studio from a different machine. An example is "<machine>.<domain>.corp.<company>.com,1433".

Upgrade the SQL Server version

To upgrade the SQL Server version, in Edit linked service page, select Recommended under Version and configure the linked service by referring to Linked service properties for the recommended version.

Differences between the recommended and the legacy version

The table below shows the differences between SQL Server using the recommended and the legacy version.

Related content

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