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How to connect to an MSSQL database

This guide will help you connect to data in a MSSQL database. This will allow you to validate and explore your data.

Prerequisites: This how-to guide assumes you have:
  • Completed the Getting Started Tutorial
  • Have a working installation of Great Expectations
  • Have access to data in a MSSQL database


1. Choose how to run the code in this guide#

Get an environment to run the code in this guide. Please choose an option below.

If you use the Great Expectations CLI, run this command to automatically generate a pre-configured Jupyter Notebook. Then you can follow along in the YAML-based workflow below:

great_expectations --v3-api datasource new

2. Install required ODBC drivers#

Follow guides from Microsoft according to your operating system:

3. Install required dependencies#

First, install the necessary dependencies for Great Expectations to connect to your MSSQL database by running the following in your terminal:

pip install sqlalchemy pyodbc

4. Add credentials#

Great Expectations provides multiple methods of using credentials for accessing databases. Options include using a file not checked into source control, environment variables, and using a cloud secret store. Please read the article Credential storage and usage options for instructions on alternatives.

For this guide we will use a connection_string like this:


5. Instantiate your project's DataContext#

Import these necessary packages and modules.

from ruamel import yaml
import great_expectations as gefrom great_expectations.core.batch import BatchRequest, RuntimeBatchRequest

Load your DataContext into memory using the get_context() method.

context = ge.get_context()

6. Configure your Datasource#

Put your connection string in this template:

datasource_yaml = """name: my_mssql_datasourceclass_name: Datasourceexecution_engine:  class_name: SqlAlchemyExecutionEngine  connection_string: mssql+pyodbc://<USERNAME>:<PASSWORD>@<HOST>:<PORT>/<DATABASE>?driver=<DRIVER>&charset=utf&autocommit=truedata_connectors:   default_runtime_data_connector_name:       class_name: RuntimeDataConnector       batch_identifiers:           - default_identifier_name   default_inferred_data_connector_name:       class_name: InferredAssetSqlDataConnector       name: whole_table"""

Run this code to test your configuration.


You will see your database tables listed as Available data_asset_names in the output of test_yaml_config().

Feel free to adjust your configuration and re-run test_yaml_config() as needed.

7. Save the Datasource configuration to your DataContext#

Save the configuration into your DataContext by using the add_datasource() function.


8. Test your new Datasource#

Verify your new Datasource by loading data from it into a Validator using a BatchRequest.

Here is an example of loading data by specifying a SQL query.

batch_request = RuntimeBatchRequest(    datasource_name="my_mssql_datasource",    data_connector_name="default_runtime_data_connector_name",    data_asset_name="default_name",  # this can be anything that identifies this data    runtime_parameters={"query": "SELECT TOP 10 * from taxi_data"},    batch_identifiers={"default_identifier_name": "default_identifier"},)context.create_expectation_suite(    expectation_suite_name="test_suite", overwrite_existing=True)validator = context.get_validator(    batch_request=batch_request, expectation_suite_name="test_suite")print(validator.head())

πŸš€πŸš€ Congratulations! πŸš€πŸš€ You successfully connected Great Expectations with your data.

Additional Notes#

To view the full scripts used in this page, see them on GitHub:

Next Steps#

Now that you've connected to your data, you'll want to work on these core skills: