Secure ML data pipelines with VPC Access Connector.

Question

Pulumi · Accepted Answer

To secure your ML data pipelines, you can create a VPC Access Connector which allows serverless services in Google Cloud to connect to your VPC network. This means that your Dataflow jobs, for example, can process data that resides in your VPC or on-premises securely. By using the VPC Access Connector, you route traffic through the connector in your VPC, ensuring a secure data transit path.

In the following Pulumi Python program, I'll define a VPC Access Connector in Google Cloud using the `gcp.vpcaccess.Connector` resource. This resource is a part of the Google Cloud provider for Pulumi and it creates a Serverless VPC Access connector that enables serverless products within Google Cloud to access resources in your VPC network.

The first step is to set up your VPC and subnet where the data you want to process resides. Then, you'll set up the VPC Access Connector to bridge between your serverless services (like Cloud Run, Cloud Functions, or Dataflow) and the VPC. This way, data can be processed while staying internal to your Google Cloud environment, which helps in maintaining security and privacy.

Here's the Pulumi program that sets up the necessary infrastructure:

```python
import pulumi
import pulumi_gcp as gcp

# Create a VPC for secure communication between serverless services and resources in your network.
vpc = gcp.compute.Network("ml-vpc", auto_create_subnetworks=False)

# Create a subnet where the VPC Access Connector will be located.
subnet = gcp.compute.Subnetwork(
    "ml-subnet",
    ip_cidr_range="10.0.0.0/28",
    region="us-central1",
    network=vpc.self_link
)

# Create the VPC Access Connector.
# This connector will allow serverless services like Google Cloud Dataflow to access the VPC securely.
vpc_access_connector = gcp.vpcaccess.Connector(
    "ml-vpc-connector",
    region=subnet.region,
    network=vpc.id,
    ip_cidr_range="10.8.0.0/28",  # Unique range for the connector within the VPC CIDR.
)

pulumi.export('vpc_id', vpc.id)
pulumi.export('subnet_id', subnet.id)
pulumi.export('vpc_access_connector_name', vpc_access_connector.name)
```

In this program, we create a custom VPC and a subnet dedicated to our machine learning workloads. This isolates the traffic and makes it easier to apply specific networking rules. The `ip_cidr_range` defines the block of IP addresses that can be assigned to resources within the subnet. The `10.0.0.0/28` and `10.8.0.0/28` are just examples, and you should choose IP ranges that fit within your network plan and avoid overlapping with other subnets.

The `vpc_access_connector` will allocate and manage resources to allow serverless services to reach the subnets in the VPC. Setting the `region` parameter to be the same as the subnet ensures that the connector is available in the location where your services are running.

Once created, these resources will allow services within Google Cloud to communicate with resources in your VPC securely, enabling you to build secure ML data pipelines.

Remember to replace the placeholders and choose the network ranges according to your organization's network planning. After deploying this infrastructure, you need to configure your serverless resources, such as Google Cloud Dataflow, to use this VPC Access Connector in their network settings.