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How to connect to data on GCS using Spark

This guide will help you connect to your data stored on Google Cloud Storage (GCS) using Spark. 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 on a GCS bucket
  • Have access to a working Spark installation

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 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. 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 Batch, BatchRequest, RuntimeBatchRequest

Please proceed only after you have instantiated your DataContext.

3. Configure your Datasource#

Using this example configuration, add in your GCS bucket and path to a directory that contains some of your data:

datasource_yaml = fr"""name: my_gcs_datasourceclass_name: Datasourceexecution_engine:    class_name: SparkDFExecutionEnginedata_connectors:    default_runtime_data_connector_name:        class_name: RuntimeDataConnector        batch_identifiers:            - default_identifier_name    default_inferred_data_connector_name:        class_name: InferredAssetGCSDataConnector        bucket_or_name: <YOUR_GCS_BUCKET_HERE>        prefix: <BUCKET_PATH_TO_DATA>        default_regex:            pattern: (.*)\.csv            group_names:                - data_asset_name"""
Authentication

It is also important to note that GCS DataConnector for Spark supports the method of authentication that requires running the gcloud command line tool in order to obtain the GOOGLE_APPLICATION_CREDENTIALS environment variable.

For more details regarding authentication, please visit the following:

Run this code to test your configuration.

context.test_yaml_config(datasource_yaml)

If you specified a GCS path containing CSV files you will see them 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.

4. 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))

5. Test your new Datasource#

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

Add the GCS path to your CSV in the path key under runtime_parameters in your RuntimeBatchRequest.

batch_request = RuntimeBatchRequest(    datasource_name="my_gcs_datasource",    data_connector_name="default_runtime_data_connector_name",    data_asset_name="<YOUR_MEANGINGFUL_NAME>",  # this can be anything that identifies this data_asset for you    runtime_parameters={"path": "<PATH_TO_YOUR_DATA_HERE>"},  # Add your GCS path here.    batch_identifiers={"default_identifier_name": "default_identifier"},)

Then load data into the Validator.

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#

If you are working with nonstandard CSVs, read one of these guides:

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: