Advanced Analytics on Azure with NetApp Files and Databricks

Question

Pulumi · Accepted Answer

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:

```python
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)
```

Explanation:
- 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.