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 V2 (Batch Kwargs) API

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 MSSQL 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://YOUR_MSSQL_USERNAME:[email protected]_MSSQL_HOST:YOUR_MSSQL_PORT/YOUR_MSSQL_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 V3 (Batch Request) API

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. Run datasource new

    From the command line, run:

    great_expectations --v3-api 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’

    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. You will be presented with a Jupyter Notebook which will guide you through the steps of creating a Datasource.

MSSQL SimpleSqlalchemyDatasource Example.

Within this notebook, you will have the opportunity to create your own yaml Datasource configuration. The following text walks through an example.

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 strings. Great Expectations uses a connection_string to connect to MSSQL databases through SQLAlchemy (reference: https://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls).

datasource_name = "my_mssql_datasource"
config = f"""
name: {datasource_name}
class_name: SimpleSqlalchemyDatasource
connection_string: mssql+pyodbc://YOUR_MSSQL_USERNAME:[email protected]_MSSQL_HOST:YOUR_MSSQL_PORT/YOUR_MSSQL_DATABASE?driver=ODBC Driver 17 for SQL Server&charset=utf&autocommit=true
introspection:
  whole_table:
    data_asset_name_suffix: __whole_table
"""

Note: Additional examples of yaml configurations for various filesystems and databases can be found in the following document: How to configure Data Context components using test_yaml_config

  1. Test your config using ``context.test_yaml_config``.

    context.test_yaml_config(
        yaml_config=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): []
    

    This means all has gone well and you can proceed with configuring your new Datasource. If something about your configuration wasn’t set up correctly, test_yaml_config will raise an error.

  2. Save the config.

    Once you are satisfied with the config of your new Datasource, you can make it a permanent part of your Great Expectations configuration. The following method will save the new Datasource to your great_expectations.yml:

    sanitize_yaml_and_save_datasource(context, config, overwrite_existing=False)
    

    Note: This will output a warning if a Datasource with the same name already exists. Use overwrite_existing=True to force overwriting.

    Note: The credentials will be stored in uncommitted/config_variables.yml to prevent checking them into version control.

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

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