1. Large Datasets Manipulation using Azure SQL Virtual Machine


    To manipulate large datasets, we can leverage Azure SQL Virtual Machines, which are optimized for extending SQL Server workloads to the cloud. It allows you to run SQL Server inside a virtual machine in the cloud. You can use it for development and testing, running production workloads, and extending on-premises SQL Server workloads to the cloud.

    The provided Pulumi resource azure-native.SqlVirtualMachine can be used to create and manage an instance of SQL Virtual Machine. Here's what you need to proceed with this setup:

    1. An Azure subscription: You'll need an active subscription to deploy the resources.
    2. A resource group: All Azure resources are housed in a resource group, which is a logical container for Azure services.
    3. The SQL Server image: You need to specify which SQL Server image (like SQL Server 2019 or 2017) you'd like to use for the SQL VM.
    4. Virtual machine size: This determines the amount of compute, memory, and storage resources that will be allocated to your VM.

    The Python program below uses Pulumi to provision an Azure SQL Virtual Machine, assuming your Pulumi and Azure environments are already configured:

    import pulumi import pulumi_azure_native as azure_native # Name for SQL VM and resources (you may want to generate or parameterize these) sql_vm_name = "sql-vm" resource_group_name = "sql-vm-rg" location = "eastus" # Choose an Azure region # Create an Azure resource group resource_group = azure_native.resources.ResourceGroup("resource_group", resource_group_name=resource_group_name, location=location) # Define the main SQL Virtual Machine resource. This is a minimal setup. # In a production scenario, you would need to consider configuring additional properties, like networking, storage, etc. sql_virtual_machine = azure_native.sqlvirtualmachine.SqlVirtualMachine("sql_virtual_machine", sqlVirtualMachineName=sql_vm_name, resourceGroupName=resource_group.name, location=resource_group.location, virtualMachineResourceId="virtual-machine-resource-id", # Replace with the desired VM resource ID sqlServerLicenseType="PAYG", # Or "AHUB" if you have hybrid benefit sqlManagement="Full", # "Full" or "LightWeight", depending on your needs sqlImageSku="Developer", # Or another edition that suits your workload sqlImageOffer="SQL2019-WS2019") # This specifies SQL Server 2019 on Windows Server 2019 # Export the SQL VM's identifier and SQL Server's default connection string pulumi.export("sql_vm_id", sql_virtual_machine.id) pulumi.export("sql_server_connection_string", pulumi.Output.all(sql_virtual_machine.fullyQualifiedDomainName, sql_virtual_machine.sqlManagement).apply( lambda values: f"Server={values[0]};Integrated Security=True;" if values[1] == "Full" else "Not Applicable"))

    In this program, we set up an Azure Resource Group first because Azure resources need to be grouped together. Then we specify the configuration for our Azure SQL Virtual Machine. We keep it simple for educational purposes. For this setup, we mention the license type, management type, SKU, and offer for the SQL Server you wish to use. At the end of the program, we export the VM's identifier and the connection string for SQL Server on that VM.

    Please note that you'll need to replace "virtual-machine-resource-id" with the actual resource ID of the Azure VM you intend to use. Also, the licensing and edition of SQL Server should be chosen based on your specific application's needs and licensing agreements.

    This program is designed for simplicity to get you started. You'll likely need to adjust it for a real-world application, such as by setting up proper networking, considering the compute and storage sizing, enabling high availability, configuring backups, considering security aspects such as firewalls and virtual network settings, and possibly using Azure Key Vault for secure credential management.