Enhanced Data Security with Databricks Managed VPC

Question

Pulumi · Accepted Answer

To enhance data security with a Databricks Managed VPC, you'll need to deploy resources within a VPC (Virtual Private Cloud) and configure your Databricks workspace to utilize this private networking setup. With Pulumi, you can automate the provisioning of these resources using infrastructure as code.

Below is a Pulumi program written in Python that will:

1. Define a VPC within your cloud provider of choice (AWS, Azure, GCP, etc.).
2. Configure the VPC with subnets, NAT gateways, and security settings.
3. Set up a Databricks workspace to operate within the VPC, ensuring that it uses the managed networking features provided by the cloud provider.
4. We'll use Databricks on AWS as an example here.

Let's go through the program step-by-step:

- We'll first create a virtual private cloud (VPC) where we'll deploy the Databricks clusters. This isolates the cluster from the public internet and other AWS resources.
- Then we'll create subnets in different Availability Zones for high availability.
- We'll also set up an internet gateway to allow communication between the VPC and the internet, along with a NAT gateway to enable internet access for resources in the private subnets.
- Next, we'll configure route tables for both public and private subnets.
- At last, we'll provision a Databricks workspace with the necessary configurations for it to use the managed VPC. We'll make sure it lies within our VPC and refer to the specific subnets where our clusters will reside.

Please note that the actual implementation might vary based on the specific requirements and cloud provider. The sample below is specific to AWS.

```python
import pulumi
import pulumi_aws as aws
import pulumi_databricks as databricks

# Create a new VPC for the Databricks managed VPC
vpc = aws.ec2.Vpc("databricks-vpc",
                  cidr_block="10.0.0.0/16",
                  enable_dns_support=True,
                  enable_dns_hostnames=True)

# Create public subnets for the VPC
public_subnet = aws.ec2.Subnet("databricks-public-subnet",
                               vpc_id=vpc.id,
                               cidr_block="10.0.1.0/24",
                               map_public_ip_on_launch=True,
                               availability_zone="us-west-2a")

# Create private subnets for the VPC
private_subnet = aws.ec2.Subnet("databricks-private-subnet",
                                vpc_id=vpc.id,
                                cidr_block="10.0.2.0/24",
                                availability_zone="us-west-2b")

# Create an Internet Gateway and attach it to the VPC
igw = aws.ec2.InternetGateway("databricks-igw",
                              vpc_id=vpc.id)

# Create a NAT Gateway to provide internet access to the private subnet
eip = aws.ec2.Eip("nat-eip", 
                   vpc=True)

nat_gateway = aws.ec2.NatGateway("databricks-nat-gateway",
                                 subnet_id=public_subnet.id,
                                 allocation_id=eip.id)

# Create route tables for public and private subnets
public_route_table = aws.ec2.RouteTable("public-route-table",
                                        vpc_id=vpc.id,
                                        routes=[aws.ec2.RouteTableRouteArgs(
                                            cidr_block="0.0.0.0/0",
                                            gateway_id=igw.id,
                                        )])

private_route_table = aws.ec2.RouteTable("private-route-table",
                                         vpc_id=vpc.id,
                                         routes=[aws.ec2.RouteTableRouteArgs(
                                             cidr_block="0.0.0.0/0",
                                             nat_gateway_id=nat_gateway.id,
                                         )])

# Associate the public route table with the public subnet
aws.ec2.RouteTableAssociation("public-rta",
                              subnet_id=public_subnet.id,
                              route_table_id=public_route_table.id)

# Associate the private route table with the private subnet
aws.ec2.RouteTableAssociation("private-rta",
                              subnet_id=private_subnet.id,
                              route_table_id=private_route_table.id)

# Create a Databricks workspace within the VPC
databricks_workspace = databricks.Workspace("workspace",
    deployment_name="databricks-workspace",
    aws_region="us-west-2",
    managed_services_cidr_block="10.0.3.0/24",  # Separate CIDR block for Databricks to manage
    network_security_group_rule_args=databricks.WorkspaceNetworkSecurityGroupRuleArgs(
        additional_security_group_ids=[]
    ),
    compute_subnet_ids=[private_subnet.id],  # Use private subnet for compute
    storage_subnet_ids=[public_subnet.id],   # Use public subnet for storage
    network_args=databricks.WorkspaceNetworkArgs(
        vpc_id=vpc.id,
    )
)

# Export outputs
pulumi.export("workspace_url", databricks_workspace.workspace_url)
```

In the program above, we've used several Pulumi resources from the AWS provider to create the VPC and networking components. We're using `aws.ec2.Vpc`, `aws.ec2.Subnet`, `aws.ec2.InternetGateway`, `aws.ec2.Eip`, `aws.ec2.NatGateway`, and `aws.ec2.RouteTable` to achieve this. The [`databricks.Workspace`](https://www.pulumi.com/registry/packages/databricks/api-docs/workspace/) resource from the Databricks provider is utilized to configure the managed Databricks workspace within the dedicated VPC we set up.

Following the code execution, the workspace URL will be exported, which you'll use to access the Databricks workspace. The infrastructure ensures enhanced security by isolating compute resources within a private network.

Make sure to replace the specific CIDR blocks and AWS region with ones that suit your infrastructure requirements.