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Create external database objects

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You can use the OPENROWSET function in SQL queries that run in the default master database of the built-in serverless SQL pool to explore data in the data lake. However, sometimes you may want to create a custom database that contains some objects that make it easier to work with external data in the data lake that you need to query frequently.

Creating a database

You can create a database in a serverless SQL pool just as you would in a SQL Server instance. You can use the graphical interface in Synapse Studio, or a CREATE DATABASE statement. One consideration is to set the collation of your database so that it supports conversion of text data in files to appropriate Transact-SQL data types.

The following example code creates a database named salesDB with a collation that makes it easier to import UTF-8 encoded text data into VARCHAR columns.

CREATE DATABASE SalesDB
    COLLATE Latin1_General_100_BIN2_UTF8

Creating an external data source

You can use the OPENROWSET function with a BULK path to query file data from your own database, just as you can in the master database; but if you plan to query data in the same location frequently, it's more efficient to define an external data source that references that location. For example, the following code creates a data source named files for the hypothetical https://mydatalake.blob.core.windows.net/data/files/ folder:

CREATE EXTERNAL DATA SOURCE files
WITH (
    LOCATION = 'https://mydatalake.blob.core.windows.net/data/files/'
)

One benefit of an external data source, is that you can simplify an OPENROWSET query to use the combination of the data source and the relative path to the folders or files you want to query:

SELECT *
FROM
    OPENROWSET(
        BULK 'orders/*.csv',
        DATA_SOURCE = 'files',
        FORMAT = 'csv',
        PARSER_VERSION = '2.0'
    ) AS orders

In this example, the BULK parameter is used to specify the relative path for all .csv files in the orders folder, which is a subfolder of the files folder referenced by the data source.

Another benefit of using a data source is that you can assign a credential for the data source to use when accessing the underlying storage, enabling you to provide access to data through SQL without permitting users to access the data directly in the storage account. For example, the following code creates a credential that uses a shared access signature (SAS) to authenticate against the underlying Azure storage account hosting the data lake.

CREATE DATABASE SCOPED CREDENTIAL sqlcred
WITH
    IDENTITY='SHARED ACCESS SIGNATURE',  
    SECRET = 'sv=xxx...';
GO

CREATE EXTERNAL DATA SOURCE secureFiles
WITH (
    LOCATION = 'https://mydatalake.blob.core.windows.net/data/secureFiles/'
    CREDENTIAL = sqlcred
);
GO

Tip

In addition to SAS authentication, you can define credentials that use managed identity (the Microsoft Entra identity used by your Azure Synapse workspace), a specific Microsoft Entra principal, or passthrough authentication based on the identity of the user running the query (which is the default type of authentication). To learn more about using credentials in a serverless SQL pool, see the Control storage account access for serverless SQL pool in Azure Synapse Analytics article in Azure Synapse Analytics documentation.

Creating an external file format

While an external data source simplifies the code needed to access files with the OPENROWSET function, you still need to provide format details for the file being access; which may include multiple settings for delimited text files. You can encapsulate these settings in an external file format, like this:

CREATE EXTERNAL FILE FORMAT CsvFormat
    WITH (
        FORMAT_TYPE = DELIMITEDTEXT,
        FORMAT_OPTIONS(
            FIELD_TERMINATOR = ',',
            STRING_DELIMITER = '"'
        )
    );
GO

After creating file formats for the specific data files you need to work with, you can use the file format to create external tables, as discussed next.

Creating an external table

When you need to perform a lot of analysis or reporting from files in the data lake, using the OPENROWSET function can result in complex code that includes data sources and file paths. To simplify access to the data, you can encapsulate the files in an external table; which users and reporting applications can query using a standard SQL SELECT statement just like any other database table. To create an external table, use the CREATE EXTERNAL TABLE statement, specifying the column schema as for a standard table, and including a WITH clause specifying the external data source, relative path, and external file format for your data.

CREATE EXTERNAL TABLE dbo.products
(
    product_id INT,
    product_name VARCHAR(20),
    list_price DECIMAL(5,2)
)
WITH
(
    DATA_SOURCE = files,
    LOCATION = 'products/*.csv',
    FILE_FORMAT = CsvFormat
);
GO

-- query the table
SELECT * FROM dbo.products;

By creating a database that contains the external objects discussed in this unit, you can provide a relational database layer over files in a data lake, making it easier for many data analysts and reporting tools to access the data by using standard SQL query semantics.

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