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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 ValidateThe act of applying an Expectation Suite to a Batch. and explore your data.

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

Steps​

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 CLICommand Line Interface, run this command to automatically generate a pre-configured Jupyter Notebook. Then you can follow along in the YAML-based workflow below:

great_expectations 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:

sqlite:///<PATH_TO_DB_FILE>

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 ge
from 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_datasource
class_name: Datasource
execution_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
include_schema_name: true
"""

Run this code to test your configuration.

context.test_yaml_config(datasource_yaml)

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.

context.add_datasource(**yaml.load(datasource_yaml))

7. Test your new Datasource​

Verify your new DatasourceProvides a standard API for accessing and interacting with data from a wide variety of source systems. by loading data from it into a ValidatorUsed to run an Expectation Suite against data. 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 main.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: