Aws To Google Cloud

How to Seamlessly Architect a Multi-Cloud Strategy from AWS to Google Cloud

Moving production workloads from AWS into Google Cloud—or running both side by side—demands more than a basic migration checklist. It’s an architectural decision with lasting impact on cost, resilience, and operational overhead.

Where Multi-Cloud Architecture Delivers Value

AWS remains the default option for global coverage and breadth of managed services. Google Cloud, however, beats AWS for managed Kubernetes (GKE), advanced ML workloads, and integrated analytics (BigQuery, Dataflow). Practical multi-cloud designs exploit those differences:

• Minimize lock-in: Critical for regulated industries and large-scale SaaS.
• Resilience: Cross-cloud DR limits blast radius; run active-standby or split traffic as needed.
• Cost arbitrage: Shift stateless workloads between providers in response to discounting or credits.
• Feature optimization: For instance, train ML models on Vertex AI, serve from AWS Lambda@Edge.

Step 1: Inventory and Analysis

Skip “spreadsheet audits” that gather dust. Use automated tools (AWS Config, CloudMapper, custom scripts with boto3) to enumerate:

• Service dependencies (e.g., Lambda tied to SQS, S3, DynamoDB).
• Custom networking (VPC peering, security groups, NACLs).
• IAM role sprawl.
• Application state: stateless vs. tightly coupled (e.g., Redis-backed microservices vs. monolithic Java EC2).

Tip: Export your AWS infrastructure graph as a DOT file—makes dependency tangles obvious.

Step 2: Migration Strategy

“Lift-and-shift” is quick but leaves optimization potential untapped. In practice:

• Containerized workloads (ECS, EKS): Migrate via Helm charts or kubectl apply to GKE. Watch out for pod-to-pod networking differences; GKE enforces stricter defaults after v1.25.
• RDS → Cloud SQL/Spanner: Check for feature parity. Not all PostgreSQL extensions are supported on Cloud SQL (e.g., pg_partman requires manual intervention).
• SQS → Pub/Sub: Latency and at-least-once semantics differ. Batch size may need tuning.

Kubernetes example:

# Migrate ECS service to GKE using Kompose (v1.26+)
kompose convert -f docker-compose.yaml
kubectl apply -f .

Known issue: Ingress setup in GKE is less flexible out-of-the-box—requires explicit managed certificates and custom annotations.

Step 3: Networking & Identity

Networks break first and worst in multi-cloud. Design:

• VPN/Interconnect: Deploy AWS Site-to-Site VPN or Direct Connect + GCP Cloud VPN/Interconnect. Beware dual-ISP, BGP routing asymmetry.
• DNS: Use Route53 + Cloud DNS with split-horizon patterns. Non-overlapping RFC1918 CIDRs are mandatory.
• Identity: SAML/OIDC federation (Okta, Azure AD) with unified MFA for both AWS IAM and GCP IAM. Not all service accounts map 1:1; sometimes must accept federated roles for CI/CD with least-privilege.

Sample Okta SAML metadata (partial):

<EntityDescriptor entityID="https://accounts.google.com/o/saml2?idpid=C024ydjsw">
  ...
</EntityDescriptor>

Tip: Rotating IAM keys across providers is often overlooked—script it in CI to prevent shadow user drift.

Step 4: Data Sync and Consistency

Multi-cloud means data sloshing between environments.

• Database migration: For PostgreSQL, pglogical or Google’s Database Migration Service (DMS) (note: DMS struggles with heavily partitioned tables >5TB).
• Object storage: Not all S3 features translate; GCS does not support S3 object locking natively—versioning and retention policies differ. Sync with rclone sync s3:bucket gcs:bucket --size-only.

For real-time sync:

# Debezium + Kafka Connect from RDS to BigQuery
export CONNECT_PRODUCER_BOOSTRAP_SERVERS="kafka:9092"
connect-distributed /debezium/config/postgres-source.json

Gotcha: Cross-region egress (> 1TB/month) can be ruinously expensive; batch data writes when possible.

Step 5: Infrastructure-as-Code & CI/CD

Terraform (v1.4+) is the de-facto standard, but beware subtle provider differences. Multi-cloud modules require careful abstraction:

variable "provider" {}
resource "aws_instance" "web" {
  count         = var.provider == "aws" ? 1 : 0
  ami           = "ami-0f5df4503b34741e4"
  instance_type = "t3.micro"
}
resource "google_compute_instance" "web" {
  count         = var.provider == "gcp" ? 1 : 0
  name          = "web-01"
  machine_type  = "e2-micro"
  zone          = "us-central1-a"
}

Pipeline: Use GitHub Actions with environment matrices, or Spinnaker + custom webhook triggers for cross-provider deploy.

Side note: CloudFormation ≠ Deployment Manager. Don’t try to keep both in sync—choose declarative tools that can be version controlled.

Step 6: Observability and Operations

Unified view across clouds prevents painful incident retros. Options:

• Metrics: Prometheus federation with cortex or Thanos. Install exporters in both clouds.
• Logging: Centralize with Elastic Cloud, or route to Datadog (datadog-agent v7+) which supports tagging by AWS/GCP resource.
• Error tracing: Jaeger/Zipkin deployed per environment works, but cross-cloud trace IDs require stickiness in load balancers.

Sample error (prometheus-node-exporter failure on GKE):

level=error msg="Error setting rlimit" code=1

Non-obvious tip: GCP Stackdriver logs can ingest AWS CloudWatch logs via custom sinks, but there’s notable parsing lag.

Use Case: Incremental Migration of Event-Driven Backend

Suppose an API backend handled by AWS Lambda with DynamoDB and S3 must incrementally move to GCP.

• Use DynamoDB Streams + DMS to mirror data to Cloud Bigtable or Firestore (watch for data type mismatches).
• Create equivalent HTTP endpoints in Cloud Functions (Node.js LTS only—other runtimes lag behind AWS in hot start times).
• Pilot canary: Migrate a subset of traffic via weighted DNS (Route53 traffic policies) and compare error budgets.

Known issue: Cold start latency for GCP Functions averages 400ms+ for JVM/Python, which may breach p95 targets. Consider container-based Cloud Run as an alternative.

Takeaway

Multi-cloud is rarely “write once, run anywhere”—trade-offs are permanent. Build with inevitable complexity in mind: automate away toil, centralize secrets and identity, and design for graceful degradation.

Pro tip: Test failover regularly (chaos engineering with Gremlin or custom scripts) to validate both provider and application behavior—not just the happy path.

Challenges will surface: permissions drift, networking quirks, bewildering cost models. But if you need flexibility, real fail-over, or want to exploit best-of-breed services, the upfront investment pays off.

Have specific quirks, failures, or optimizations from your own AWS–GCP projects? Drop them in the comments or send a PR to the playbook.