1. Data Lakes for Analytics on AI Outcomes Using Azure Data Lake Storage Gen2


    Data Lakes are central repositories that allow you to store all your structured and unstructured data at any scale. Azure Data Lake Storage Gen2 (ADLS Gen2) is a set of capabilities dedicated to big data analytics, built on Azure Blob storage. It is designed to enable operational efficiencies through a robust security model, cost management, high availability, and ease of deployment.

    When integrating Data Lakes for analytics on AI outcomes, Azure provides a suite of services that you can use together to store vast amounts of data, apply analytics and AI, and drive insights. Among the key services for this are Azure Data Lake Storage Gen2, Azure HDInsight, Azure Databricks, and Azure Synapse Analytics.

    In this Pulumi program, we will create an Azure Data Lake Storage Gen2 account, which is the foundation for building a data lake. We will also instantiate a File System (container) within the ADLS account where the data will be stored.

    Here's what we will do in our Pulumi program:

    1. Import necessary modules for Azure and Pulumi.
    2. Set up the resource group where all resources will live.
    3. Create an ADLS Gen2 storage account.
    4. Set up a File System (Blob Container) in the storage account for organizing the data into a hierarchical namespace.

    Below is the Pulumi program written in Python that demonstrates how to set up these resources:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup("ai-analytics-rg") # Create an Azure Data Lake Storage Gen2 Account storage_account = azure_native.storage.StorageAccount("aidatalakestorage", resource_group_name=resource_group.name, kind="StorageV2", # ADLS Gen2 requires the "StorageV2" kind sku=azure_native.storage.SkuArgs( name="Standard_LRS" # Locally-redundant storage ), location=resource_group.location, is_hns_enabled=True # This flag enables the hierarchical namespace for ADLS Gen2 ) # Create a Data Lake File System (Container) within the storage account file_system = azure_native.storage.BlobContainer("ai-data-container", account_name=storage_account.name, resource_group_name=resource_group.name, public_access="None" # Data will not be publicly accessible ) # Export the storage account and file system endpoints as stack outputs pulumi.export("storage_account_endpoint", storage_account.primary_endpoints) pulumi.export("file_system_url", pulumi.Output.concat( "https://", storage_account.name, ".dfs.core.windows.net/", file_system.name) )

    In this program:

    • We first create a resource group named ai-analytics-rg, which will contain our resources.
    • Next, we define a storage account with the name aidatalakestorage, specifying the kind as StorageV2 which is required for ADLS Gen2, and the SKU for the type of redundancy we require for our data.
    • The is_hns_enabled flag is set to True to enable the hierarchical namespace feature which is crucial for ADLS Gen2.
    • We then create a BlobContainer named ai-data-container. Note that we keep public_access set to None, ensuring our data is private.
    • Finally, we export stack outputs like the storage_account_endpoint and a constructed file_system_url which are the endpoints to access the storage account and file system.

    This is a minimal setup to get started with Azure Data Lake Storage Gen2 for data lake solutions. You can extend this with additional configurations for networking, access control, and integrating other services like Azure HDInsight or Azure Synapse for big data analytics and AI processing.

    Remember to install the Pulumi Azure Native package if you haven't already:

    pip install pulumi_azure_native

    To run this program, save it in a file (e.g., main.py), ensure you have an Azure account configured with Pulumi, and execute it using the Pulumi CLI:

    pulumi up

    This command will provision the resources as per the script in your Azure cloud environment.