Register and maintain datastores

Data ingestion and storage are critical components of any Data Science solution. Azure provides a robust set of services for designing and implementing data storage solutions that cater to the needs of your data scientists and machine learning models. In this article, we will explore how to register and maintain datastores in Azure to support your data science workflows.

What are datastores in Azure?

Datastores in Azure are repositories where you can store and access your data. Azure provides various options to create and manage datastores, including Azure Blob storage, Azure Data Lake Storage, Azure SQL Database, Azure PostgreSQL, and many more. These datastores help you organize and manage your data efficiently, making it easily accessible for analysis and modeling.

Registering a Datastore in Azure Machine Learning

Azure Machine Learning allows you to register datastores and make them available throughout your data science projects. To register a datastore, you need to define its connection details and access credentials. Let’s see how to register an Azure Blob storage account as a datastore using Python code:

from azureml.core import Workspace, Datastore

# Register the Blob storage account as a datastore
blob_datastore = Datastore.register_azure_blob_container(
workspace=workspace,
datastore_name="my_blob_datastore",
container_name="my_blob_container",
account_name="my_storage_account",
account_key="my_storage_key"
)

In the code snippet above, we first import the necessary Azure Machine Learning classes. Then, we load the workspace using the from_config() method, which loads the workspace details from the configuration file. Next, we use the register_azure_blob_container() method to register an Azure Blob storage account as a datastore. We provide the necessary details such as the workspace, datastore name, container name, storage account name, and storage account key.

Maintaining datastores

Once you have registered a datastore, you can easily access and manage it within your data science projects. Azure Machine Learning provides convenient methods to retrieve and use registered datastores. Here’s an example that demonstrates how to retrieve a registered datastore and use it to work with data:

# Retrieve a registered datastore
datastore = Datastore.get(workspace, datastore_name="my_blob_datastore")

# Read a file from the mounted datastore
file_path = os.path.join(mounted_path, "data", "data.csv")
df = pd.read_csv(file_path)

In the code snippet, we first use the Datastore.get() method to retrieve the registered datastore by providing the workspace and the datastore name. Once we have the datastore, we can perform various operations on it. In this case, we upload a file to the datastore using the upload_files() method, specifying the file path and the target path within the datastore. We then mount the datastore using the mount() method, which creates a local directory that represents the datastore. Finally, we can read a file from the mounted datastore and perform further operations on it.

Maintaining datastores also involves managing access credentials and performing operations like updating, deleting, or refreshing the datastore. Refer to the Azure Machine Learning documentation for detailed information on managing datastores in Azure.

Conclusion

Datastores play a crucial role in managing and organizing data for your Data Science solution. In this article, we explored how to register and maintain datastores in Azure using Azure Machine Learning. We saw how to register an Azure Blob storage account as a datastore and perform operations on it. Datastores provide a streamlined way for data scientists to access and work with data, thereby enabling efficient and scalable data science workflows.