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How to connect to a SQLite database

This guide will help you connect to data in a SQLite 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 SQLite 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 dependencies#

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

pip install sqlalchemy

3. Configure the URL for your SQLite database#

Since SQLite connects to file-based databases, the URL format is slightly different from other DBs. For this guide we will use a connection_string that looks like this:


For more details on different ways to specify database files and information on how to connect to an in-memory SQLite database, please refer to the documentation on SQLAlchemy.

4. 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()

5. Configure your Datasource#

Put your connection string in this template:

datasource_yaml = f"""name: my_sqlite_datasourceclass_name: Datasourceexecution_engine:  class_name: SqlAlchemyExecutionEngine  connection_string: sqlite://<PATH_TO_DB_FILE>data_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.

6. Save the Datasource configuration to your DataContext#

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


7. 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_sqlite_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 * from yellow_tripdata_sample_2019_01 LIMIT 10"    },    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"

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: