1. Advanced Analytics on Azure with NetApp Files and Databricks


    Advanced analytics platforms allow you to analyze large datasets to uncover insights and trends, drive business decisions, and create data-driven applications. On Azure, advanced analytics can be achieved using a combination of services like Azure Databricks for big data processing and machine learning, and Azure NetApp Files for high-performance file storage.

    Here's a basic roadmap for setting up an advanced analytics environment on Azure with Databricks and NetApp Files using Pulumi in Python:

    1. Azure NetApp Files - This service provides high-performance file storage. It's typically used when you need to share a common data source between different compute nodes, which is a common scenario in analytics workflows.

    2. Azure Databricks Workspace - Databricks is an analytics platform optimized for the Microsoft Azure cloud services platform. Databricks integrates with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

    As part of setting up this environment, you will:

    • Create Azure NetApp Files storage and define volume where data to be analyzed will reside.
    • Set up an Azure Databricks workspace to perform the analytics tasks.

    Now, let's write a Pulumi program in Python to provision these resources:

    import pulumi import pulumi_azure_native as azure_native # Basic configuration for the resources project_name = 'advanced-analytics' location = 'East US' # Specify the Azure region you want your resources to be in # Create an Azure Resource Group to logically group the resources resource_group = azure_native.resources.ResourceGroup(f'{project_name}-rg', resource_group_name=f'{project_name}-rg', location=location) # Creating the Azure NetApp Account netapp_account = azure_native.netapp.Account(f'{project_name}-netapp-account', account_name=f'{project_name}-netapp-account', resource_group_name=resource_group.name, location=location) # Creating the NetApp Capacity Pool capacity_pool = azure_native.netapp.CapacityPool(f'{project_name}-capacity-pool', pool_name=f'{project_name}-capacity-pool', resource_group_name=resource_group.name, account_name=netapp_account.name, location=location, service_level='Premium', # Choose based on performance requirements size=4398046511104) # Minimum size for Premium is 4TiB # Creating the NetApp volume inside the Capacity Pool netapp_volume = azure_native.netapp.Volume(f'{project_name}-netapp-volume', volume_name=f'{project_name}-netapp-volume', resource_group_name=resource_group.name, account_name=netapp_account.name, pool_name=capacity_pool.name, location=location, usage_threshold=107374182400, # 100 GiB protocols=['NFSv3'], # Choose protocol based on requirements # This is an example subnet id, replace with the actual subnet id subnet_id="/subscriptions/sub-id/resourceGroups/rg/providers/Microsoft.Network/virtualNetworks/vnet/subnets/subnet") # Creating the Azure Databricks Workspace databricks_workspace = azure_native.databricks.Workspace(f'{project_name}-databricks', workspace_name=f'{project_name}-databricks', resource_group_name=resource_group.name, location=location, sku=azure_native.databricks.SkuArgs(name='standard')) # Export the IDs of the created resources pulumi.export('resource_group_id', resource_group.id) pulumi.export('netapp_account_id', netapp_account.id) pulumi.export('capacity_pool_id', capacity_pool.id) pulumi.export('netapp_volume_id', netapp_volume.id) pulumi.export('databricks_workspace_id', databricks_workspace.id)


    • First, we're creating a resource group to hold all the resources we will create; this helps with organization and isolating environments.
    • Next, we create an Azure NetApp account which is required before creating any volumes.
    • After that, we create a capacity pool within the NetApp account. The pool provides a collection of volumes that share the same performance characteristics.
    • We then create a NetApp volume within the capacity pool to be used as the shared storage for our data.
    • Finally, we create an Azure Databricks workspace which is where the analytics processing will take place.

    Remember to replace the placeholder /subscriptions/sub-id/resourceGroups/rg/providers/Microsoft.Network/virtualNetworks/vnet/subnets/subnet with the actual Subnet ID where your NetApp Files will be deployed.

    To run this Pulumi program, save it in a file named __main__.py, and execute pulumi up in the same directory. Ensure that you've configured the Azure credentials and selected the correct Pulumi stack before running the command.