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How to connect to data on Azure Blob Storage using Pandas

This guide will help you connect to your data stored on Microsoft Azure Blob Storage (ABS) using Pandas. 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 an ABS container

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

Load your DataContext into memory using the get_context() method.

context = ge.get_context()

3. Configure your Datasource#

Great Expectations provides two types of DataConnectors classes for connecting to ABS: InferredAssetAzureDataConnector and ConfiguredAssetAzureDataConnector

  • An InferredAssetAzureDataConnector utilizes regular expressions to infer data_asset_names by evaluating filename patterns that exist in your bucket. This DataConnector, along with a RuntimeDataConnector, is provided as a default when utilizing our Jupyter Notebooks.
  • A ConfiguredAssetAzureDataConnector requires an explicit listing of each DataAsset you want to connect to. This allows for more granularity and control than its Inferred counterpart but also requires a more complex setup.

As the InferredAssetDataConnectors have fewer options and are generally simpler to use, we recommend starting with them.

We've detailed example configurations for both options in the next section for your reference.

Authentication

It is also important to note that the ABS DataConnectors for Pandas support two (mutually exclusive) methods of authentication. You should be aware of the following options when configuring your own environment:

  • account_url key in the azure_options dictionary
    • This is the default option and what is used throughout this guide.
  • conn_str key in the azure_options dictionary
  • In all cases, the AZURE_CREDENTIAL environment variable is required.

The azure_options dictionary is also responsible for storing any **kwargs you wish to pass to the ABS BlobServiceClient connection object.

For more details regarding authentication and access using Python, please visit the following:

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

The below configuration is representative of the default setup you'll see when preparing your own environment.
datasource_yaml = fr"""name: my_azure_datasourceclass_name: Datasourceexecution_engine:    class_name: PandasExecutionEngine    azure_options:        account_url: <YOUR_ACCOUNT_URL> # or `conn_str`        credential: <YOUR_CREDENTIAL>   # if using a protected containerdata_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"""

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

Add the name of the data asset to the data_asset_name in your BatchRequest.

batch_request = BatchRequest(    datasource_name="my_azure_datasource",    data_connector_name="default_inferred_data_connector_name",    data_asset_name="<YOUR_DATA_ASSET_NAME>",)

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")

🚀🚀 Congratulations! 🚀🚀 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:

To review the source code of these DataConnectors, also visit GitHub: