Private Google Access for GCP Data Engineer Exam

When designing network architectures in Google Cloud, you face a fundamental security decision: should your virtual machines access Google services like Cloud Storage and BigQuery through public IP addresses and the internet, or should they use Private Google Access to communicate privately? This choice affects security posture, compliance requirements, attack surface, and operational complexity. Understanding Private Google Access is essential for the Professional Data Engineer exam and for building production-ready data pipelines in GCP.

The challenge centers on balancing accessibility with security. Your data engineers need VMs to load datasets into BigQuery, read from Cloud Storage buckets, and call various Google Cloud APIs. The question becomes whether these VMs should be exposed to the public internet or remain completely internal.

The Traditional Approach: Public IP Addresses

The straightforward method involves assigning public IP addresses to your virtual machines. When a VM has an external IP, it can reach any internet destination, including Google Cloud APIs and services. The VM makes requests that travel through your VPC, exit through Cloud NAT or the public IP, traverse the public internet, and reach Google's publicly accessible API endpoints.

Consider a scenario where a genomics research lab runs Compute Engine VMs that process DNA sequencing data. These VMs need to read raw sequencing files from Cloud Storage buckets and write analysis results back. With public IP addresses assigned, the configuration is straightforward. Each VM can directly call Cloud Storage APIs without additional network configuration.

This approach offers simplicity during initial setup. Network administrators familiar with traditional infrastructure can apply familiar patterns. Troubleshooting connectivity issues becomes easier because you can use standard internet diagnostic tools. The VM can also access third-party APIs, software repositories, and external services without additional configuration.

Here's what a typical VM configuration looks like with a public IP:

gcloud compute instances create genomics-processor \
  --zone=us-central1-a \
  --machine-type=n1-standard-4 \
  --subnet=default \
  --address=35.224.123.45

The VM receives the external IP 35.224.123.45 and can immediately access Cloud Storage, BigQuery, and other Google services through public endpoints.

Drawbacks of Public IP Addresses

The security implications of this approach become significant at scale. Every VM with a public IP represents a potential attack vector. Even with firewall rules restricting inbound traffic, the VM remains visible to internet scanners and potential attackers. Security teams must maintain vigilant patch management, monitor for suspicious access attempts, and ensure no accidental exposure of sensitive services.

Compliance frameworks like HIPAA, PCI-DSS, and various data sovereignty regulations often require minimizing public internet exposure for systems handling sensitive data. The genomics lab processing patient health information might face regulatory scrutiny for having research VMs with public IPs, even if those IPs are well-protected.

Cost considerations also emerge. Each public IP address carries a small hourly charge. For a data engineering team running hundreds of ephemeral VMs for batch processing jobs, these charges accumulate. Traffic exiting your VPC to reach Google services over the internet counts as egress traffic, which can incur substantial charges depending on volume and destination.

Consider this scenario: your VMs are processing 10 TB of transaction logs per day, reading from Cloud Storage and writing aggregated results to BigQuery. With public IPs, this traffic exits your VPC, hits the public internet (even though it's destined for Google services), and returns. The network path is inefficient and the egress costs add up.

The Alternative: Private Google Access

Private Google Access fundamentally changes the connectivity model. When enabled at the subnet level, VMs within that subnet can reach Google APIs and services using only their internal IP addresses. The traffic never leaves Google's network infrastructure. There's no need for public IPs, NAT gateways, or internet routing.

The key architectural difference is that Private Google Access creates a private pathway from your VPC directly to Google service endpoints. The VM makes API calls using its internal IP, the request routes through Google's internal network fabric, and reaches the service without ever touching the public internet.

Returning to our genomics lab, you can configure the subnet where processing VMs run with Private Google Access enabled:

gcloud compute networks subnets update genomics-processing-subnet \
  --region=us-central1 \
  --enable-private-ip-google-access

Now VMs in this subnet can access Cloud Storage, BigQuery, Cloud Pub/Sub, and other Google services without public IPs. A VM configuration becomes:

gcloud compute instances create genomics-processor-private \
  --zone=us-central1-a \
  --machine-type=n1-standard-4 \
  --subnet=genomics-processing-subnet \
  --no-address

The --no-address flag ensures no external IP is assigned. The VM receives only an internal IP like 10.128.0.5. Despite having no public connectivity, it can still read from Cloud Storage buckets, execute BigQuery queries, and interact with Google Cloud APIs.

The security benefits are immediate and substantial. The VM is completely isolated from the public internet. It can't be scanned, probed, or attacked from external sources. The attack surface shrinks dramatically. Security teams can focus defensive resources on protecting the actual entry points to your GCP environment rather than hardening hundreds of individual VMs.

Compliance becomes significantly easier. When auditors ask whether systems processing protected health information or payment card data have public internet exposure, the answer is clearly no. The architecture inherently supports zero-trust principles and defense-in-depth strategies.

How Cloud Storage and BigQuery Handle Private Google Access

Understanding how specific Google Cloud services respond to Private Google Access requests reveals important implementation details. When you enable Private Google Access, Google Cloud configures routing to direct API calls to private IP ranges rather than public endpoints.

Cloud Storage provides a specific IP range (199.36.153.8/30) that serves as the private endpoint. When your VM without a public IP calls the Cloud Storage API, Google's internal routing directs that traffic to this private range. The request never exits Google's network perimeter. The same mechanism applies to BigQuery, Cloud Pub/Sub, Cloud Spanner, and other managed services.

BigQuery demonstrates the practical impact particularly well. Imagine a subscription box service that runs nightly ETL jobs on Compute Engine VMs. These jobs query BigQuery to extract subscriber behavior data, transform it using Python scripts, and load results back into BigQuery for the data science team.

With Private Google Access enabled, the VM executes queries like:

SELECT 
  subscriber_id,
  COUNT(DISTINCT box_shipment_date) as total_boxes_received,
  SUM(box_value) as lifetime_value,
  MAX(box_shipment_date) as last_shipment_date
FROM `subscription-data.shipments.deliveries`
WHERE shipment_status = 'delivered'
  AND box_shipment_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
GROUP BY subscriber_id
HAVING total_boxes_received >= 3;

This query processes potentially millions of rows. The data never traverses the public internet. It moves through Google's private network from BigQuery's storage to the VM, gets processed, and returns to BigQuery. The performance is better because Google's internal network provides lower latency and higher throughput than internet routing. The cost is lower because you avoid egress charges.

Cloud Storage operations benefit similarly. When the ETL job needs to read a 500 GB CSV file from a bucket, the transfer happens entirely within Google's infrastructure:

from google.cloud import storage

client = storage.Client()
bucket = client.bucket('subscription-analytics-raw')
blob = bucket.blob('subscriber_interactions_2024_01.csv')

blob.download_to_filename('/data/interactions.csv')

The download proceeds over the private connection. Network performance remains consistent and predictable because you're not competing with variable internet conditions. Security teams can verify that sensitive subscriber data never left Google's controlled network environment.

The architectural decision Google made here is important. Rather than requiring complex VPN configurations or dedicated interconnects, Private Google Access uses intelligent routing within the existing VPC infrastructure. It's a subnet-level toggle that fundamentally changes how API traffic flows without requiring changes to application code or individual VM configurations.

Detailed Scenario: Agricultural Monitoring Platform

A precision agriculture company operates a platform that collects sensor data from thousands of fields across multiple farming regions. Soil moisture sensors, weather stations, and drone imagery all feed into their analytics pipeline. The architecture includes hundreds of Compute Engine VMs running data ingestion workers that pull sensor readings from Cloud Pub/Sub. Processing jobs clean and validate the sensor data. BigQuery tables store historical sensor readings and crop yield outcomes. Cloud Storage buckets hold drone imagery and processed datasets. Dataflow jobs handle real-time anomaly detection.

In the initial architecture, all VMs had public IP addresses. The network team configured Cloud NAT for outbound connectivity and maintained careful firewall rules. Monthly costs included 150 VMs with public IPs at $438 per month, network egress for 50 TB per month of data transfers to Cloud Storage and BigQuery costing approximately $4,500 per month, and Cloud NAT charges of approximately $200 per month.

Security audits repeatedly flagged the public IP exposure. Even though inbound traffic was blocked, the security team had to maintain monitoring for each public IP, respond to scanner probes, and ensure no accidental service exposure.

The team redesigned the architecture using Private Google Access:

# Enable Private Google Access on the processing subnet
gcloud compute networks subnets update sensor-processing \
  --region=us-central1 \
  --enable-private-ip-google-access

# Recreate VMs without external IPs
gcloud compute instances create sensor-worker-001 \
  --zone=us-central1-a \
  --machine-type=n1-standard-2 \
  --subnet=sensor-processing \
  --no-address \
  --service-account=sensor-worker@project.iam.gserviceaccount.com \
  --scopes=https://www.googleapis.com/auth/cloud-platform

After migration, the cost structure changed. Public IP charges were eliminated, saving $438 per month. Network egress charges were eliminated for Google service traffic, saving approximately $4,500 per month. Cloud NAT was removed from this subnet, saving $200 per month.

Total monthly savings exceeded $5,000. The security posture improved dramatically. VMs became invisible to internet scanners. Compliance documentation simplified because the processing infrastructure had zero public internet exposure.

The one limitation appeared when VMs needed to download Python packages from PyPI or pull Docker images from external registries. For this, the team created a separate subnet with Cloud NAT for VMs that needed occasional external connectivity, keeping the bulk of the processing infrastructure completely private.

Decision Framework: When to Use Each Approach

The choice between Private Google Access and public IPs depends on several factors. Private Google Access provides VMs isolated from the internet with minimal attack surface, makes compliance easier by satisfying data isolation requirements, eliminates public IP charges and egress costs for Google services, delivers lower latency via Google's private network, but can't reach non-Google internet services and requires subnet configuration with separate NAT for external needs. Troubleshooting is limited to internal tools and VPC Flow Logs.

Public IP addresses leave VMs visible to the internet and require hardening. They often need justification and additional controls for compliance. You'll pay hourly IP charges and egress costs for API traffic. Performance faces variable latency through internet routing. However, you get full internet connectivity with simpler initial configuration and can use standard internet diagnostic tools.

Choose Private Google Access when your VMs primarily interact with Google Cloud services, security and compliance are priorities, you want to minimize costs, and you can separate external connectivity needs into dedicated subnets.

Use public IPs when VMs need frequent access to external APIs or services, you require simpler network architecture during prototyping, or you have legacy applications expecting direct internet connectivity.

For data engineering workloads in GCP, Private Google Access is often the better default choice. Data pipelines typically move data between Cloud Storage, BigQuery, Dataflow, and other Google services. The security benefits and cost savings outweigh the additional network design considerations.

Conclusion

Private Google Access represents a deliberate architectural choice between security and simplicity. By enabling private connectivity to Google services, you eliminate attack vectors, reduce costs, and improve compliance posture. The trade-off requires thinking carefully about network segmentation and planning for scenarios where VMs do need external connectivity.

The Professional Data Engineer exam tests your understanding of when and why to apply Private Google Access. You'll need to recognize scenarios where security requirements demand private connectivity, calculate cost implications of different network architectures, and design VPC configurations that support data pipeline requirements while maintaining appropriate isolation.

Thoughtful engineering means understanding that network architecture decisions cascade through security, cost, and operational complexity. Private Google Access isn't universally better than public IPs. It's a tool that becomes invaluable when applied to the right workloads with the right requirements. For readers looking for comprehensive preparation that covers Private Google Access and the full spectrum of networking, security, and data engineering concepts, check out the Professional Data Engineer course for structured study materials and practice scenarios.