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:
Obtained database credentials for MSSQL, including username, password, hostname, and database.
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.
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
Run datasource new
From the command line, run:
great_expectations datasource new
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
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
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
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:YOUR_MSSQL_PASSWORD@YOUR_MSSQL_HOST:YOUR_MSSQL_PORT/YOUR_MSSQL_DATABASE?driver=ODBC Driver 17 for SQL Server&charset=utf&autocommit=true
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:
Learned how to configure a Data Context using test_yaml_config
Obtained database credentials for MSSQL, including username, password, hostname, and database.
To add a MSSQL datasource, do the following:
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.
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
Run datasource new
From the command line, run:
great_expectations --v3-api datasource new
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
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
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:YOUR_MSSQL_PASSWORD@YOUR_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
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 aself_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.- 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.