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Version: 0.15.50

How to connect to data on Azure Blob Storage using Spark

This guide will help you connect to your data stored on Microsoft Azure Blob Storage (ABS) using Spark. 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 on an ABS container
  • 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 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. Instantiate your project's DataContext​

Import these necessary packages and modules.

from ruamel import yaml

import great_expectations as gx
from great_expectations.core.batch import Batch, BatchRequest
Load your DataContext into memory

Use one of the guides below based on your deployment:

Please proceed only after you have instantiated your DataContext.

3. Configure your Datasource​

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

datasource_yaml = rf"""
name: my_azure_datasource
class_name: Datasource
execution_engine:
    class_name: SparkDFExecutionEngine
    azure_options:
        account_url: <your_account_url> # or `conn_str`
        credential: <your_credential>   # if using a protected container
data_connectors:
    default_runtime_data_connector_name:
        class_name: RuntimeDataConnector
        batch_identifiers:
            - default_identifier_name
    default_inferred_data_connector_name:
        class_name: InferredAssetAzureDataConnector
        azure_options:
            account_url: <your_account_url> # or `conn_str`
            credential: <your_credential>   # if using a protected container
        container: <your_azure_container_here>
        name_starts_with: <container_path_to_data>
        default_regex:
            pattern: (.*)\.csv
            group_names:
                - data_asset_name
"""
Authentication

It is also important to note that ABS DataConnector for Spark supports the method of authentication called Windows Azure Storage Blob Secure ("WASBS"), which requires the AZURE_ACCESS_KEY environment variable to be set.

For more details regarding storing credentials for use with Great Expectations see: How to configure credentials

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 an ABS 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 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 Batch RequestProvided to a Datasource in order to create a Batch..

Add the name of the Data AssetA collection of records within a Datasource which is usually named based on the underlying data system and sliced to correspond to a desired specification. to the data_asset_name in your BatchRequest. 

batch_request = BatchRequest(
    datasource_name="version-0.15.50 my_azure_datasource",
    data_connector_name="version-0.15.50 default_inferred_data_connector_name",
    data_asset_name="version-0.15.50 <your_data_asset_name>",
    batch_spec_passthrough={"reader_method": "csv", "reader_options": {"header": True}},
)

Then load data into the Validator.

context.add_or_update_expectation_suite(expectation_suite_name="version-0.15.50 test_suite")
validator = context.get_validator(
    batch_request=batch_request, expectation_suite_name="version-0.15.50 test_suite"
)
print(validator.head())

🚀🚀 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: