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Version: 0.14.13

How to connect to a BigQuery database

This guide will help you connect to data in a BigQuery 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:


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 BigQuery database by running the following in your terminal:

pip install sqlalchemy-bigquery

3. 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 manager. Please read the article How to Configure Credentials for instructions on alternatives.

For this guide we will use a connection_string like this:


4. Instantiate your project's DataContext

Import these necessary packages and modules.

import os

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_bigquery_datasource
class_name: Datasource
class_name: SqlAlchemyExecutionEngine
connection_string: bigquery://<GCP_PROJECT_NAME>/<BIGQUERY_DATASET>
class_name: RuntimeDataConnector
- default_identifier_name
class_name: InferredAssetSqlDataConnector
include_schema_name: true

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 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.


Currently BigQuery does not allow for the creation of temporary tables as the result of a query. As a workaround, Great Expectations allows you to pass in a string to use as a table name. It will then use this string to create a named permanent table as a "temporary" table, with the name passed in as a batch_spec_passthrough parameter. The table will be created in the location specified in the connection_string of your execution_engine. In the following example we are using a table named ge_temp.

batch_request = RuntimeBatchRequest(
data_asset_name="default_name", # this can be anything that identifies this data
runtime_parameters={"query": "SELECT * from demo.taxi_data LIMIT 10"},
batch_identifiers={"default_identifier_name": "default_identifier"},
"bigquery_temp_table": "ge_temp"
}, # this is the name of the table you would like to use a 'temp_table'

expectation_suite_name="test_suite", overwrite_existing=True
validator = context.get_validator(
batch_request=batch_request, expectation_suite_name="test_suite"

🚀🚀 Congratulations! 🚀🚀 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: