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

Tip

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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:

Supported capabilities	IR
Copy activity (source/sink)	① ②
Mapping data flow (source/sink)	①
Lookup activity	① ②
GetMetadata activity	① ②
Script activity	① ②
Stored procedure activity	① ②

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.

Important

The data source must support the NVARCHAR data type since it affects the data encoding when a non-universal coding is being applied on the data.

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

Tip

If you hit an error with the error code "UserErrorFailedToConnectToSqlServer" and a message like "The session limit for the database is XXX and has been reached," add Pooling=false to your connection string and try again.

Recommended version

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

Property	Description	Required
type	The type property must be set to SqlServer.	Yes
server	The name or network address of the SQL server instance you want to connect to.	Yes
database	The name of the database.	Yes
authenticationType	The type used for authentication. Allowed values are SQL (default), Windows and UserAssignedManagedIdentity (only for SQL Server on Azure VMs). Go to the relevant authentication section on specific properties and prerequisites.	Yes
alwaysEncryptedSettings	Specify alwaysencryptedsettings information that's needed to enable Always Encrypted to protect sensitive data stored in SQL server by using either managed identity or service principal. For more information, see the JSON example following the table and Using Always Encrypted section. If not specified, the default always encrypted setting is disabled.	No
encrypt	Indicate whether TLS encryption is required for all data sent between the client and server. Options: mandatory (for true, default)/optional (for false)/strict.	No
trustServerCertificate	Indicate whether the channel will be encrypted while bypassing the certificate chain to validate trust.	No
hostNameInCertificate	The host name to use when validating the server certificate for the connection. When not specified, the server name is used for certificate validation.	No
connectVia	This integration runtime is used to connect to the data store. Learn more from Prerequisites section. If not specified, the default Azure integration runtime is used.	No

For additional connection properties, see the table below:

Property	Description	Required
applicationIntent	The application workload type when connecting to a server. Allowed values are ReadOnly and ReadWrite.	No
connectTimeout	The length of time (in seconds) to wait for a connection to the server before terminating the attempt and generating an error.	No
connectRetryCount	The number of reconnections attempted after identifying an idle connection failure. The value should be an integer between 0 and 255.	No
connectRetryInterval	The amount of time (in seconds) between each reconnection attempt after identifying an idle connection failure. The value should be an integer between 1 and 60.	No
loadBalanceTimeout	The minimum time (in seconds) for the connection to live in the connection pool before the connection being destroyed.	No
commandTimeout	The default wait time (in seconds) before terminating the attempt to execute a command and generating an error.	No
integratedSecurity	The allowed values are true or false. When specifying false, indicate whether userName and password are specified in the connection. When specifying true, indicates whether the current Windows account credentials are used for authentication.	No
failoverPartner	The name or address of the partner server to connect to if the primary server is down.	No
maxPoolSize	The maximum number of connections allowed in the connection pool for the specific connection.	No
minPoolSize	The minimum number of connections allowed in the connection pool for the specific connection.	No
multipleActiveResultSets	The allowed values are true or false. When you specify true, an application can maintain multiple active result sets (MARS). When you specify false, an application must process or cancel all result sets from one batch before it can execute any other batches on that connection.	No
multiSubnetFailover	The allowed values are true or false. If your application is connecting to an AlwaysOn availability group (AG) on different subnets, setting this property to true provides faster detection of and connection to the currently active server.	No
packetSize	The size in bytes of the network packets used to communicate with an instance of server.	No
pooling	The allowed values are true or false. When you specify true, the connection will be pooled. When you specify false, the connection will be explicitly opened every time the connection is requested.	No

SQL authentication

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

Property	Description	Required
userName	The user name to be used when connecting to server.	Yes
password	The password for the user name. Mark this field as SecureString to store it securely. Or, you can reference a secret stored in Azure Key Vault.	No

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:

Property	Description	Required
userName	Specify a user name. An example is domainname\username.	Yes
password	Specify a password for the user account you specified for the user name. Mark this field as SecureString to store it securely. Or, you can reference a secret stored in Azure Key Vault.	Yes

Note

Windows authentication is not supported in data flow.

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"
        }
    }
}

User-assigned managed identity authentication

Note

The user-assigned managed identity authentication only applies to SQL Server on Azure VMs.

A data factory or Synapse workspace can be associated with a user-assigned managed identities that represents the service when authenticating to other resources in Azure. You can use this managed identity for SQL Server on Azure VMs authentication. The designated factory or Synapse workspace can access and copy data from or to your database by using this identity.

To use user-assigned managed identity authentication, in addition to the generic properties that are described in the preceding section, specify the following properties:

Property	Description	Required
credentials	Specify the user-assigned managed identity as the credential object.	Yes

You also need to follow the steps below:

1. Grant permissions to your user-assigned managed identity.

2. Enable Microsoft Entra authentication to your SQL Server on Azure VMs.

3. Create contained database users for the user-assigned managed identity. Connect to the database from or to which you want to copy data by using tools like SQL Server Management Studio, with a Microsoft Entra identity that has at least ALTER ANY USER permission. Run the following T-SQL:

   CREATE USER [your_resource_name] FROM EXTERNAL PROVIDER;
   

4. Create one or multiple user-assigned managed identities and grant the user-assigned managed identity needed permissions as you normally do for SQL users and others. Run the following code. For more options, see this document.

   ALTER ROLE [role name] ADD MEMBER [your_resource_name];
   

5. Assign one or multiple user-assigned managed identities to your data factory and create credentials for each user-assigned managed identity.

6. Configure a SQL Server linked service.

Example

{
    "name": "SqlServerLinkedService",
    "properties": {
        "type": "SqlServer",
        "typeProperties": {
            "server": "<name or network address of the SQL server instance>",
            "database": "<database name>",
            "encrypt": "<encrypt>",
            "trustServerCertificate": false,
            "authenticationType": "UserAssignedManagedIdentity",
            "credential": {
                "referenceName": "credential1",
                "type": "CredentialReference"
            }
        },
        "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:

Property	Description	Required
type	The type property must be set to SqlServer.	Yes
alwaysEncryptedSettings	Specify alwaysencryptedsettings information that's needed to enable Always Encrypted to protect sensitive data stored in SQL server by using either managed identity or service principal. For more information, see Using Always Encrypted section. If not specified, the default always encrypted setting is disabled.	No
connectVia	This integration runtime is used to connect to the data store. Learn more from Prerequisites section. If not specified, the default Azure integration runtime is used.	No

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:

Property	Description	Required
connectionString	Specify connectionString information that's needed to connect to the SQL Server database. Specify a login name as your user name, and ensure the database that you want to connect is mapped to this login.	Yes
password	If you want to put a password in Azure Key Vault, pull the password configuration out of the connection string. For more information, see Store credentials in Azure Key Vault.	No

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:

Property	Description	Required
connectionString	Specify connectionString information that's needed to connect to the SQL Server database.	Yes
userName	Specify a user name. An example is domainname\username.	Yes
password	Specify a password for the user account you specified for the user name. Mark this field as SecureString to store it securely. Or, you can reference a secret stored in Azure Key Vault.	Yes

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:

Property	Description	Required
type	The type property of the dataset must be set to SqlServerTable.	Yes
schema	Name of the schema.	No for source, Yes for sink
table	Name of the table/view.	No for source, Yes for sink
tableName	Name of the table/view with schema. This property is supported for backward compatibility. For new workload, use schema and table.	No for source, Yes for sink

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

Tip

To load data from SQL Server efficiently by using data partitioning, learn more from Parallel copy from SQL database.

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:

Property	Description	Required
type	The type property of the copy activity source must be set to SqlSource.	Yes
sqlReaderQuery	Use the custom SQL query to read data. An example is select * from MyTable.	No
sqlReaderStoredProcedureName	This property is the name of the stored procedure that reads data from the source table. The last SQL statement must be a SELECT statement in the stored procedure.	No
storedProcedureParameters	These parameters are for the stored procedure. Allowed values are name or value pairs. The names and casing of parameters must match the names and casing of the stored procedure parameters.	No
isolationLevel	Specifies the transaction locking behavior for the SQL source. The allowed values are: ReadCommitted, ReadUncommitted, RepeatableRead, Serializable, Snapshot. If not specified, the database's default isolation level is used. Refer to this doc for more details.	No
partitionOptions	Specifies the data partitioning options used to load data from SQL Server. Allowed values are: None (default), PhysicalPartitionsOfTable, and DynamicRange. When a partition option is enabled (that is, not None), the degree of parallelism to concurrently load data from SQL Server is controlled by the parallelCopies setting on the copy activity.	No
partitionSettings	Specify the group of the settings for data partitioning. Apply when the partition option isn't None.	No
Under partitionSettings:
partitionColumnName	Specify the name of the source column in integer or date/datetime type (int, smallint, bigint, date, smalldatetime, datetime, datetime2, or datetimeoffset) that will be used by range partitioning for parallel copy. If not specified, the index or the primary key of the table is auto-detected and used as the partition column. Apply when the partition option is DynamicRange. If you use a query to retrieve the source data, hook ?DfDynamicRangePartitionCondition in the WHERE clause. For an example, see the Parallel copy from SQL database section.	No
partitionUpperBound	The maximum value of the partition column for partition range splitting. This value is used to decide the partition stride, not for filtering the rows in table. All rows in the table or query result will be partitioned and copied. If not specified, copy activity auto detect the value. Apply when the partition option is DynamicRange. For an example, see the Parallel copy from SQL database section.	No
partitionLowerBound	The minimum value of the partition column for partition range splitting. This value is used to decide the partition stride, not for filtering the rows in table. All rows in the table or query result will be partitioned and copied. If not specified, copy activity auto detect the value. Apply when the partition option is DynamicRange. For an example, see the Parallel copy from SQL database section.	No

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

Tip

Learn more about the supported write behaviors, configurations, and best practices from Best practice for loading data into SQL Server.

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:

Property	Description	Required
type	The type property of the copy activity sink must be set to SqlSink.	Yes
preCopyScript	This property specifies a SQL query for the copy activity to run before writing data into SQL Server. It's invoked only once per copy run. You can use this property to clean up the preloaded data.	No
tableOption	Specifies whether to automatically create the sink table if not exists based on the source schema. Auto table creation is not supported when sink specifies stored procedure. Allowed values are: none (default), autoCreate.	No
sqlWriterStoredProcedureName	The name of the stored procedure that defines how to apply source data into a target table. This stored procedure is invoked per batch. For operations that run only once and have nothing to do with source data, for example, delete or truncate, use the preCopyScript property. See example from Invoke a stored procedure from a SQL sink.	No
storedProcedureTableTypeParameterName	The parameter name of the table type specified in the stored procedure.	No
sqlWriterTableType	The table type name to be used in the stored procedure. The copy activity makes the data being moved available in a temp table with this table type. Stored procedure code can then merge the data that's being copied with existing data.	No
storedProcedureParameters	Parameters for the stored procedure. Allowed values are name and value pairs. Names and casing of parameters must match the names and casing of the stored procedure parameters.	No
writeBatchSize	Number of rows to insert into the SQL table per batch. Allowed values are integers for the number of rows. By default, the service dynamically determines the appropriate batch size based on the row size.	No
writeBatchTimeout	The wait time for the insert, upsert and stored procedure operation to complete before it times out. Allowed values are for the timespan. An example is "00:30:00" for 30 minutes. If no value is specified, the timeout defaults to "00:30:00".	No
maxConcurrentConnections	The upper limit of concurrent connections established to the data store during the activity run. Specify a value only when you want to limit concurrent connections.	No
WriteBehavior	Specify the write behavior for copy activity to load data into SQL Server Database. The allowed value is Insert and Upsert. By default, the service uses insert to load data.	No
upsertSettings	Specify the group of the settings for write behavior. Apply when the WriteBehavior option is Upsert.	No
Under upsertSettings:
useTempDB	Specify whether to use the global temporary table or physical table as the interim table for upsert. By default, the service uses global temporary table as the interim table. value is true.	No
interimSchemaName	Specify the interim schema for creating interim table if physical table is used. Note: user need to have the permission for creating and deleting table. By default, interim table will share the same schema as sink table. Apply when the useTempDB option is False.	No
keys	Specify the column names for unique row identification. Either a single key or a series of keys can be used. If not specified, the primary key is used.	No

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.

Scenario	Suggested settings
Full load from large table, with physical partitions.	Partition option: Physical partitions of table. During execution, the service automatically detects the physical partitions, and copies data by partitions. To check if your table has physical partition or not, you can refer to this query.
Full load from large table, without physical partitions, while with an integer or datetime column for data partitioning.	Partition options: Dynamic range partition. Partition column (optional): Specify the column used to partition data. If not specified, the primary key column is used. Partition upper bound and partition lower bound (optional): Specify if you want to determine the partition stride. This is not for filtering the rows in table, all rows in the table will be partitioned and copied. If not specified, copy activity auto detects the values and it can take long time depending on MIN and MAX values. It is recommended to provide upper bound and lower bound. For example, if your partition column "ID" has values range from 1 to 100, and you set the lower bound as 20 and the upper bound as 80, with parallel copy as 4, the service retrieves data by 4 partitions - IDs in range <=20, [21, 50], [51, 80], and >=81, respectively.
Load a large amount of data by using a custom query, without physical partitions, while with an integer or date/datetime column for data partitioning.	Partition options: Dynamic range partition. Query: SELECT * FROM <TableName> WHERE ?DfDynamicRangePartitionCondition AND <your_additional_where_clause>. Partition column: Specify the column used to partition data. Partition upper bound and partition lower bound (optional): Specify if you want to determine the partition stride. This is not for filtering the rows in table, all rows in the query result will be partitioned and copied. If not specified, copy activity auto detect the value. For example, if your partition column "ID" has values range from 1 to 100, and you set the lower bound as 20 and the upper bound as 80, with parallel copy as 4, the service retrieves data by 4 partitions- IDs in range <=20, [21, 50], [51, 80], and >=81, respectively. Here are more sample queries for different scenarios: 1. Query the whole table: SELECT * FROM <TableName> WHERE ?DfDynamicRangePartitionCondition 2. Query from a table with column selection and additional where-clause filters: SELECT <column_list> FROM <TableName> WHERE ?DfDynamicRangePartitionCondition AND <your_additional_where_clause> 3. Query with subqueries: SELECT <column_list> FROM (<your_sub_query>) AS T WHERE ?DfDynamicRangePartitionCondition AND <your_additional_where_clause> 4. Query with partition in subquery: SELECT <column_list> FROM (SELECT <your_sub_query_column_list> FROM <TableName> WHERE ?DfDynamicRangePartitionCondition) AS T

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.

Note

To access on premise SQL Server, you need to use Azure Data Factory or Synapse workspace Managed Virtual Network using a private endpoint. Refer to this tutorial for detailed steps.

Source transformation

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

Name	Description	Required	Allowed values	Data flow script property
Table	If you select Table as input, data flow fetches all the data from the table specified in the dataset.	No	-	-
Query	If you select Query as input, specify a SQL query to fetch data from source, which overrides any table you specify in dataset. Using queries is a great way to reduce rows for testing or lookups. Order By clause is not supported, but you can set a full SELECT FROM statement. You can also use user-defined table functions. select * from udfGetData() is a UDF in SQL that returns a table that you can use in data flow. Query example: Select * from MyTable where customerId > 1000 and customerId < 2000	No	String	query
Batch size	Specify a batch size to chunk large data into reads.	No	Integer	batchSize
Isolation Level	Choose one of the following isolation levels: - Read Committed - Read Uncommitted (default) - Repeatable Read - Serializable - None (ignore isolation level)	No	READ_COMMITTED READ_UNCOMMITTED REPEATABLE_READ SERIALIZABLE NONE	isolationLevel
Enable incremental extract	Use this option to tell ADF to only process rows that have changed since the last time that the pipeline executed.	No	-	-
Incremental date column	When using the incremental extract feature, you must choose the date/time column that you wish to use as the watermark in your source table.	No	-	-
Enable native change data capture(Preview)	Use this option to tell ADF to only process delta data captured by SQL change data capture technology since the last time that the pipeline executed. With this option, the delta data including row insert, update and deletion will be loaded automatically without any incremental date column required. You need to enable change data capture on SQL Server before using this option in ADF. For more information about this option in ADF, see native change data capture.	No	-	-
Start reading from beginning	Setting this option with incremental extract will instruct ADF to read all rows on first execution of a pipeline with incremental extract turned on.	No	-	-

Tip

The common table expression (CTE) in SQL is not supported in the mapping data flow Query mode, because the prerequisite of using this mode is that queries can be used in the SQL query FROM clause but CTEs cannot do this. To use CTEs, you need to create a stored procedure using the following query:

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

Then use the Stored procedure mode in the source transformation of the mapping data flow and set the @query like example with CTE as (select 'test' as a) select * from CTE. Then you can use CTEs as expected.

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.

Name	Description	Required	Allowed values	Data flow script property
Update method	Specify what operations are allowed on your database destination. The default is to only allow inserts. To update, upsert, or delete rows, an Alter row transformation is required to tag rows for those actions.	Yes	true or false	deletable insertable updateable upsertable
Key columns	For updates, upserts and deletes, key column(s) must be set to determine which row to alter. The column name that you pick as the key will be used as part of the subsequent update, upsert, delete. Therefore, you must pick a column that exists in the Sink mapping.	No	Array	keys
Skip writing key columns	If you wish to not write the value to the key column, select "Skip writing key columns".	No	true or false	skipKeyWrites
Table action	Determines whether to recreate or remove all rows from the destination table prior to writing. - None: No action will be done to the table. - Recreate: The table will get dropped and recreated. Required if creating a new table dynamically. - Truncate: All rows from the target table will get removed.	No	true or false	recreate truncate
Batch size	Specify how many rows are being written in each batch. Larger batch sizes improve compression and memory optimization, but risk out of memory exceptions when caching data.	No	Integer	batchSize
Pre and Post SQL scripts	Specify multi-line SQL scripts that will execute before (pre-processing) and after (post-processing) data is written to your Sink database.	No	String	preSQLs postSQLs

Tip

1. It's recommended to break single batch scripts with multiple commands into multiple batches.
2. Only Data Definition Language (DDL) and Data Manipulation Language (DML) statements that return a simple update count can be run as part of a batch. Learn more from Performing batch operations

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.

SQL Server data type	Data Factory interim data type
bigint	Int64
binary	Byte[]
bit	Boolean
char	String, Char[]
date	DateTime
Datetime	DateTime
datetime2	DateTime
Datetimeoffset	DateTimeOffset
Decimal	Decimal
FILESTREAM attribute (varbinary(max))	Byte[]
Float	Double
image	Byte[]
int	Int32
money	Decimal
nchar	String, Char[]
ntext	String, Char[]
numeric	Decimal
nvarchar	String, Char[]
real	Single
rowversion	Byte[]
smalldatetime	DateTime
smallint	Int16
smallmoney	Decimal
sql_variant	Object
text	String, Char[]
time	TimeSpan
timestamp	Byte[]
tinyint	Int16
uniqueidentifier	Guid
varbinary	Byte[]
varchar	String, Char[]
xml	String

Note

For data types that map to the Decimal interim type, currently Copy activity supports precision up to 28. If you have data that requires precision larger than 28, consider converting to a string in a SQL query.

When copying data from SQL Server using Azure Data Factory, the bit data type is mapped to the Boolean interim data type. If you have data that need to be kept as the bit data type, use queries with T-SQL CAST or CONVERT.

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.

Note

SQL Server Always Encrypted supports below scenarios:

1. Either source or sink data stores is using managed identity or service principal as key provider authentication type.
2. Both source and sink data stores are using managed identity as key provider authentication type.
3. Both source and sink data stores are using the same service principal as key provider authentication type.

Note

Currently, SQL Server Always Encrypted is only supported for source transformation in mapping data flows.

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.

Recommended version	Legacy version
Support TLS 1.3 via encrypt as strict.	TLS 1.3 is not supported.

Related content

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