Copy data to or from Azure SQL Database by using Azure Data Factory

This article outlines how to copy data to and from Azure SQL Database. To learn about Azure Data Factory, read the introductory article.

Supported capabilities

This Azure SQL Database connector is supported for the following activities:

Specifically, this Azure SQL Database connector supports these functions:

  • Copying data by using SQL authentication and Azure Active Directory (Azure AD) Application token authentication with a service principal or managed identities for Azure resources.
  • As a source, retrieving data by using a SQL query or a stored procedure.
  • As a sink, appending data to a destination table or invoking a stored procedure with custom logic during the copy.

Note

Azure SQL Database Always Encrypted isn't supported by this connector now. To work around, you can use a generic ODBC connector and a SQL Server ODBC driver via a self-hosted integration runtime. Follow this guidance with ODBC driver download and connection string configurations.

Important

If you copy data by using the Azure Data Factory integration runtime, configure an Azure SQL Server firewall so that Azure services can access the server. If you copy data by using a self-hosted integration runtime, configure the Azure SQL Server firewall to allow the appropriate IP range. This range includes the machine's IP that's used to connect to Azure SQL Database.

Get started

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

The following sections provide details about properties that are used to define Azure Data Factory entities specific to an Azure SQL Database connector.

Linked service properties

These properties are supported for an Azure SQL Database linked service:

Property Description Required
type The type property must be set to AzureSqlDatabase. Yes
connectionString Specify information needed to connect to the Azure SQL Database instance for the connectionString property.
Mark this field as SecureString to store it securely in Azure Data Factory. You also can put a password or service principal key in Azure Key Vault. If it's SQL authentication, pull the password configuration out of the connection string. For more information, see the JSON example following the table and Store credentials in Azure Key Vault.
Yes
servicePrincipalId Specify the application's client ID. Yes, when you use Azure AD authentication with a service principal
servicePrincipalKey Specify the application's key. Mark this field as SecureString to store it securely in Azure Data Factory or reference a secret stored in Azure Key Vault. Yes, when you use Azure AD authentication with a service principal
tenant Specify the tenant information, like the domain name or tenant ID, under which your application resides. Retrieve it by hovering the mouse in the upper-right corner of the Azure portal. Yes, when you use Azure AD authentication with a service principal
connectVia This integration runtime is used to connect to the data store. You can use the Azure integration runtime or a self-hosted integration runtime if your data store is located in a private network. If not specified, the default Azure integration runtime is used. No

For different authentication types, refer to the following sections on prerequisites and JSON samples, respectively:

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.

SQL authentication

Linked service example that uses SQL authentication

{
    "name": "AzureSqlDbLinkedService",
    "properties": {
        "type": "AzureSqlDatabase",
        "typeProperties": {
            "connectionString": {
                "type": "SecureString",
                "value": "Server=tcp:<servername>.database.windows.net,1433;Database=<databasename>;User ID=<username>@<servername>;Password=<password>;Trusted_Connection=False;Encrypt=True;Connection Timeout=30"
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Password in Azure Key Vault

{
    "name": "AzureSqlDbLinkedService",
    "properties": {
        "type": "AzureSqlDatabase",
        "typeProperties": {
            "connectionString": {
                "type": "SecureString",
                "value": "Server=tcp:<servername>.database.windows.net,1433;Database=<databasename>;User ID=<username>@<servername>;Trusted_Connection=False;Encrypt=True;Connection Timeout=30"
            },
            "password": { 
                "type": "AzureKeyVaultSecret", 
                "store": { 
                    "referenceName": "<Azure Key Vault linked service name>", 
                    "type": "LinkedServiceReference" 
                }, 
                "secretName": "<secretName>" 
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Service principal authentication

To use a service principal-based Azure AD application token authentication, follow these steps:

  1. Create an Azure Active Directory application from the Azure portal. Make note of the application name and the following values that define the linked service:

    • Application ID
    • Application key
    • Tenant ID
  2. Provision an Azure Active Directory administrator for your Azure SQL Server on the Azure portal if you haven't already done so. The Azure AD administrator must be an Azure AD user or Azure AD group, but it can't be a service principal. This step is done so that, in the next step, you can use an Azure AD identity to create a contained database user for the service principal.

  3. Create contained database users for the service principal. Connect to the database from or to which you want to copy data by using tools like SQL Server Management Studio, with an Azure AD identity that has at least ALTER ANY USER permission. Run the following T-SQL:

    CREATE USER [your application name] FROM EXTERNAL PROVIDER;
    
  4. Grant the service principal needed permissions as you normally do for SQL users or others. Run the following code. For more options, see this document.

    EXEC sp_addrolemember [role name], [your application name];
    
  5. Configure an Azure SQL Database linked service in Azure Data Factory.

Linked service example that uses service principal authentication

{
    "name": "AzureSqlDbLinkedService",
    "properties": {
        "type": "AzureSqlDatabase",
        "typeProperties": {
            "connectionString": {
                "type": "SecureString",
                "value": "Server=tcp:<servername>.database.windows.net,1433;Database=<databasename>;Connection Timeout=30"
            },
            "servicePrincipalId": "<service principal id>",
            "servicePrincipalKey": {
                "type": "SecureString",
                "value": "<service principal key>"
            },
            "tenant": "<tenant info, e.g. microsoft.onmicrosoft.com>"
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Managed identities for Azure resources authentication

A data factory can be associated with a managed identity for Azure resources that represents the specific data factory. You can use this managed identity for Azure SQL Database authentication. The designated factory can access and copy data from or to your database by using this identity.

To use managed identity authentication, follow these steps.

  1. Provision an Azure Active Directory administrator for your Azure SQL Server on the Azure portal if you haven't already done so. The Azure AD administrator can be an Azure AD user or an Azure AD group. If you grant the group with managed identity an admin role, skip steps 3 and 4. The administrator has full access to the database.

  2. Create contained database users for the Azure Data Factory managed identity. Connect to the database from or to which you want to copy data by using tools like SQL Server Management Studio, with an Azure AD identity that has at least ALTER ANY USER permission. Run the following T-SQL:

    CREATE USER [your Data Factory name] FROM EXTERNAL PROVIDER;
    
  3. Grant the Data Factory managed identity needed permissions as you normally do for SQL users and others. Run the following code. For more options, see this document.

    EXEC sp_addrolemember [role name], [your Data Factory name];
    
  4. Configure an Azure SQL Database linked service in Azure Data Factory.

Example

{
    "name": "AzureSqlDbLinkedService",
    "properties": {
        "type": "AzureSqlDatabase",
        "typeProperties": {
            "connectionString": {
                "type": "SecureString",
                "value": "Server=tcp:<servername>.database.windows.net,1433;Database=<databasename>;Connection Timeout=30"
            }
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Dataset properties

For a full list of sections and properties available to define datasets, see Datasets. This section provides a list of properties supported by the Azure SQL Database dataset.

To copy data from or to Azure SQL Database, the following properties are supported:

Property Description Required
type The type property of the dataset must be set to AzureSqlTable. Yes
tableName The name of the table or view in the Azure SQL Database instance that the linked service refers to. No for source, Yes for sink

Dataset properties example

{
    "name": "AzureSQLDbDataset",
    "properties":
    {
        "type": "AzureSqlTable",
        "linkedServiceName": {
            "referenceName": "<Azure SQL Database linked service name>",
            "type": "LinkedServiceReference"
        },
        "schema": [ < physical schema, optional, retrievable during authoring > ],
        "typeProperties": {
            "tableName": "MyTable"
        }
    }
}

Copy activity properties

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

Azure SQL Database as the source

To copy data from Azure SQL Database, 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 AzureSqlSource. "SqlSource" type is still supported for backward compatibility. Yes
sqlReaderQuery This property uses the custom SQL query to read data. An example is select * from MyTable. No
sqlReaderStoredProcedureName 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 Parameters 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

Points to note:

  • If sqlReaderQuery is specified for AzureSqlSource, the copy activity runs this query against the Azure SQL Database source to get the data. You also can specify a stored procedure by specifying sqlReaderStoredProcedureName and storedProcedureParameters if the stored procedure takes parameters.
  • If you don't specify either sqlReaderQuery or sqlReaderStoredProcedureName, the columns defined in the "structure" section of the dataset JSON are used to construct a query. The query select column1, column2 from mytable runs against Azure SQL Database. If the dataset definition doesn't have "structure," all columns are selected from the table.

SQL query example

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

Stored procedure example

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

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

Azure SQL Database as the sink

Tip

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

To copy data to Azure SQL Database, 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 AzureSqlSink. "SqlSink" type is still supported for backward compatibility. Yes
writeBatchSize Number of rows to insert into the SQL table per batch.
The allowed value is integer (number of rows). By default, Azure Data Factory dynamically determines the appropriate batch size based on the row size.
No
writeBatchTimeout The wait time for the batch insert operation to finish before it times out.
The allowed value is timespan. An example is “00:30:00” (30 minutes).
No
preCopyScript Specify a SQL query for the copy activity to run before writing data into Azure SQL Database. It's invoked only once per copy run. Use this property to clean up the preloaded data. 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.
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

Example 1: Append data

"activities":[
    {
        "name": "CopyToAzureSQLDatabase",
        "type": "Copy",
        "inputs": [
            {
                "referenceName": "<input dataset name>",
                "type": "DatasetReference"
            }
        ],
        "outputs": [
            {
                "referenceName": "<Azure SQL Database output dataset name>",
                "type": "DatasetReference"
            }
        ],
        "typeProperties": {
            "source": {
                "type": "<source type>"
            },
            "sink": {
                "type": "AzureSqlSink",
                "writeBatchSize": 100000
            }
        }
    }
]

Example 2: Invoke a stored procedure during copy

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

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

Best practice for loading data into Azure SQL Database

When you copy data into Azure SQL Database, 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 an entire dimension table each time.
  • Write with custom logic: I need extra processing before the final insertion into the destination table.

Refer to the respective sections about how to configure in Azure Data Factory and best practices.

Append data

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

Upsert data

Option 1: When you have a large amount of data to copy, use the following approach to do an upsert:

  • First, use a database scoped temporary table to bulk load all records by using the copy activity. Because operations against database scoped temporary tables aren't logged, you can load millions of records in seconds.
  • Run a stored procedure activity in Azure Data Factory to apply a MERGE or INSERT/UPDATE statement. Use the temp table as the source to perform all updates or inserts as a single transaction. In this way, the number of round trips and log operations is reduced. At the end of the stored procedure activity, the temp table can be truncated to be ready for the next upsert cycle.

As an example, in Azure Data Factory, you can create a pipeline with a Copy activity chained with a Stored Procedure activity. The former copies data from your source store into an Azure SQL Database temporary table, for example, ##UpsertTempTable, as the table name in the dataset. Then the latter invokes a stored procedure to merge source data from the temp table into the target table and clean up the temp table.

Upsert

In your database, define a stored procedure with MERGE logic, like the following example, which is pointed to from the previous stored procedure activity. Assume that the target is the Marketing table with three columns: ProfileID, State, and Category. Do the upsert based on the ProfileID column.

CREATE PROCEDURE [dbo].[spMergeData]
AS
BEGIN
	MERGE TargetTable AS target
	USING ##UpsertTempTable AS source
	ON (target.[ProfileID] = source.[ProfileID])
	WHEN MATCHED THEN
		UPDATE SET State = source.State
    WHEN NOT matched THEN
    	INSERT ([ProfileID], [State], [Category])
      VALUES (source.ProfileID, source.State, source.Category);
    
    TRUNCATE TABLE ##UpsertTempTable
END

Option 2: You also can choose to invoke a stored procedure within the copy activity. This approach runs each row in the source table instead of using bulk insert as the default approach in the copy activity, which isn't appropriate for large-scale upsert.

Overwrite the entire table

You can configure the preCopyScript property in the copy activity sink. In this case, for each copy activity that runs, Azure Data Factory 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, for large scale, you can do one of two things:

  • Load to a database scoped temporary table and then invoke a stored procedure.
  • Invoke a stored procedure during copy.

Invoke a stored procedure from a SQL sink

When you copy data into Azure SQL Database, you also can configure and invoke a user-specified stored procedure with additional parameters. The stored procedure feature takes advantage of table-valued parameters.

Tip

Invoking a stored procedure processes the data row by row instead of by using a bulk operation, which we don't recommend for large-scale copy. Learn more from Best practice for loading data into Azure SQL Database.

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 Azure SQL 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. In Azure Data Factory, define the SQL sink section in the copy activity as follows:

    "sink": {
        "type": "AzureSqlSink",
        "SqlWriterStoredProcedureName": "spOverwriteMarketing",
        "storedProcedureTableTypeParameterName": "Marketing",
        "SqlWriterTableType": "MarketingType",
        "storedProcedureParameters": {
            "category": {
                "value": "ProductA"
            }
        }
    }
    

Mapping data flow properties

Learn details from source transformation and sink transformation in mapping data flow.

Data type mapping for Azure SQL Database

When data is copied from or to Azure SQL Database, the following mappings are used from Azure SQL Database data types to Azure Data Factory interim data types. To learn how the copy activity maps the source schema and data type to the sink, see Schema and data type mappings.

Azure SQL Database data type Azure 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 Byte
uniqueidentifier Guid
varbinary Byte[]
varchar String, Char[]
xml Xml

Note

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

Next steps

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