How to configure a MSSQL Datasource

This guide shows how to connect to a MSSQL Datasource. Great Expectations uses SqlAlchemy to connect to MSSQL, and relies further on the PyODBC driver.

Steps

Show Docs for Stable API (up to 0.12.x)

Prerequisites: This how-to guide assumes you have already:

  1. Install the required ODBC drivers

    Follow guides from Microsoft according to your operating system. We have included additional links to relevant resources for connecting to MSSQL databases in the Additional Information section below.

  2. Install the required python modules

    If you have not already done so, install required modules for connecting to MSSQL.

    pip install sqlalchemy
    pip install pyodbc
    
  3. Run datasource new

    From the command line, run:

    great_expectations datasource new
    
  4. Choose “Relational database (SQL)”

    What data would you like Great Expectations to connect to?
        1. Files on a filesystem (for processing with Pandas or Spark)
        2. Relational database (SQL)
    : 2
    
  5. Choose ‘other’ and provide a connection string

    Which database backend are you using?
        1. MySQL
        2. Postgres
        3. Redshift
        4. Snowflake
        5. BigQuery
        6. other - Do you have a working SQLAlchemy connection string?
    : 6
    
  6. Give your Datasource a name

    When prompted, provide a custom name for your Snowflake data source, or hit Enter to accept the default.

    Give your new Datasource a short name.
     [my_database]: mssql_db
    
  7. Enter connection information

    When prompted, enter a connection string to use to connect to your datasource. Note that we add a query parameter to our connection string to specify the driver: driver=ODBC Driver 17 for SQL Server

    Next, we will configure database credentials and store them in the `my_database` section
    of this config file: great_expectations/uncommitted/config_variables.yml:
    
    What is the url/connection string for the sqlalchemy connection?
    (reference: https://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls)
    : mssql+pyodbc://<<username>>:<<password>>@<<host>>:<<port>>/<<database>>?driver=ODBC Driver 17 for SQL Server&charset=utf&autocommit=true
    
  8. Save your new configuration

    Great Expectations will now add a new Datasource 'mssql_db' to your deployment, by adding this entry to your great_expectations.yml:
    
      mssql_db:
        credentials: ${my_database}
        data_asset_type:
          class_name: SqlAlchemyDataset
          module_name: great_expectations.dataset
        class_name: SqlAlchemyDatasource
        module_name: great_expectations.datasource
    
    The credentials will be saved in uncommitted/config_variables.yml under the key 'mssql_db'
    

Show Docs for Experimental API (0.13)

Prerequisites: This how-to guide assumes you have already:

To add a MSSQL datasource, do the following:

  1. Install the required ODBC drivers

    Follow guides from Microsoft according to your operating system. We have included additional links to relevant resources for connecting to MSSQL databases in the Additional Information section below.

  2. Install the required python modules

    If you have not already done so, install required modules for connecting to MSSQL.

    pip install sqlalchemy
    pip install pyodbc
    
  3. Create or copy a yaml config

    Parameters can be set as strings, or passed in as environment variables. In the following example, a yaml config is configured for a SimpleSqlalchemyDatasource with associated credentials passed in as environment variables. GE uses a connection_string to connect to MSSQL databases through sqlalchemy (reference: https://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls).

    SimpleSqlalchemyDatasource is a sub-class of Datasource that automatically configures a SqlDataConnector, and is one you will probably want to use when connecting to data in an sql database. (More information on Datasources in GE 0.13 can found in Core Great Expectations Concepts document.)

    This example also uses introspection to configure the datasource, where each table in the database is associated with its own data_asset. A deeper explanation on the different modes of building data_asset from data (introspective / inferred vs configured) can be found in the Core Great Expectations Concepts document.

    Also, additional examples of yaml configurations for various filesystems and databases can be found in the following document: How to configure DataContext components using test_yaml_config

    config = f"""
    class_name: SimpleSqlalchemyDatasource
    connection_string: mssql+pyodbc://{user_name}:{password}@{host}:{port}/{database}?driver=ODBC Driver 17 for SQL Server&charset=utf&autocommit=true
    introspection:
        whole_table:
            data_asset_name_suffix: __whole_table
    """
    
  4. Run context.test_yaml_config.

    context.test_yaml_config(
        name="my_mssql_datasource",
        yaml_config=my_config
    )
    

    When executed, test_yaml_config will instantiate the component and run through a self_check procedure to verify that the component works as expected.

    The resulting output will look something like this:

    Attempting to instantiate class from config...
        Instantiating as a Datasource, since class_name is SimpleSqlalchemyDatasource
        Successfully instantiated SimpleSqlalchemyDatasource
    
    Execution engine: SqlAlchemyExecutionEngine
    Data connectors:
        whole_table : InferredAssetSqlDataConnector
    
        Available data_asset_names (1 of 1):
                    imdb_100k_main__whole_table (1 of 1): [{}]
    
        Unmatched data_references (0 of 0): []
    
        Choosing an example data reference...
            Reference chosen: {}
    
        Fetching batch data...
        [(58098,)]
    
                Showing 5 rows
           movieId                               title                                         genres
        0        1                    Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy\r
        1        2                      Jumanji (1995)                   Adventure|Children|Fantasy\r
        2        3             Grumpier Old Men (1995)                               Comedy|Romance\r
        3        4            Waiting to Exhale (1995)                         Comedy|Drama|Romance\r
        4        5  Father of the Bride Part II (1995)                                       Comedy\r
    

    This means all has went well and you can proceed with exploring the data sets in your new MSSQL datasource.

    Note : In the current example, the yaml config will only create a connection to the datasource for the current session. After you exit python, the datasource and configuration will be gone. To make the datasource and configuration persistent, please add information to great_expectations.yml in your great_expectations/ directory.

Additional notes

The following blog post provides a useful overview of using SqlAlchemy to connect to MSSQL.

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