Mastering Cross-Cloud Data Migration: AWS to GCP—A Real-World Guide

Migrating workloads between AWS and Google Cloud Platform (GCP) isn’t just a technical exercise—it’s a series of risk-laden decisions that demands careful mapping, operational discipline, and a healthy skepticism for “lift and shift” shortcuts. Below is a distilled reference for practitioners leading real migrations and seeking practical, detail-oriented steps.

Motivation: Why Shift Workloads from AWS to GCP?

GCP often wins out with AI/ML integrations, analytics tooling (BigQuery, Vertex AI), and innovative networking (Dedicated Interconnect). Pure cost play? Sometimes. Usually, it’s about aligning technical differentiators with new business goals, not chasing platform hype.

Common triggers:

• Lock-in fatigue: Lower exit costs and contracting flexibility.
• Latency or data residency: Deploy closer to emergent user bases.
• Modernization: Migrate legacy managed services to Kubernetes (GKE), or adopt native GCP Big Data pipelines.

Gotcha: GCP’s resource hierarchy and IAM model diverge dramatically from AWS—no one-to-one “checkbox” clones here.

1. Inventory and Baseline Assessment

Start with a raw asset inventory. Tools like AWS Config (aws configservice describe-config-rules), ec2 describe-instances, and CloudMapper help untangle what’s in play.

Critical questions:

• Which EC2 instances are stateful?
• Are RDS backups or cross-region replicas in use?
• Is S3 storing static assets, backup sets, or app data with last-modified requirements?
• Are there legacy IAM role policies, or Lambda triggers with external SQS dependencies?

Sample asset map:

Don’t trust documentation alone—run live enumeration scripts where possible, double-checking for “shadow IT” resources missed by tagging or ownership sprawl.

2. Mapping AWS Services to GCP Equivalents

“Like for like” isn’t always an efficiency win. Document which features are required per workload, not just the service name.

Known issue: GCP IAM “primitive roles” (Owner, Editor, Viewer) are too broad. Use custom roles and least-privilege, even for migration/POC phases.

3. Data Migration: Choosing the Right Tool for the Job

For object storage, Storage Transfer Service handles S3 → GCS at scale, supports overwrite/append modes, and can preserve ACLs (most of the time). For block and relational storage, introduce a sync phase to reduce total cutover downtime.

Object Storage (S3 → GCS):

gcloud transfer jobs create \
  --source-s3-bucket="my-bucket" \
  --destination-gcs-bucket="my-gcs-bucket" \
  --status=enabled

Known gap: Storage Transfer Service can choke on S3 object locks or certain encrypted objects. Audit source bucket for these flags first.

Relational DBs:

• Use Google’s Database Migration Service (gcloud database-migration jobs) for managed Postgres/MySQL—enables continuous replication, catch-up sync, and verification.
• For SQL Server, consider external tools or dump/restore with sqlcmd scripts, but expect more manual failover steps.

Non-obvious tip: If you have massive data volumes but limited migration windows, seed data to GCP via GSUTIL’s -m (multithreaded) mode, then turn on DMS for change data capture in the final days.

4. Security and IAM Translation

Expect mismatches—AWS IAM policies (JSON) versus GCP’s role bindings and service account-centric access.

Checklist:

• Generate principle-of-least-privilege custom roles in GCP. For system-to-system (app) workloads, prefer dedicated service accounts per app.
• Map AWS Security Groups to GCP VPC firewall rules; don’t overlook implied deny/allow differences.
• Revisit identity federation (e.g., SAML/OIDC) if user auth spans both clouds.

Example policy mapping:
AWS S3 read access via IAM role:

{
   "Effect": "Allow",
   "Action": "s3:GetObject",
   "Resource": "arn:aws:s3:::my-bucket/*"
}

GCP equivalent (IAM, via gcloud):

gcloud projects add-iam-policy-binding my-gcp-project \
  --member="serviceAccount:app-sa@my-gcp-project.iam.gserviceaccount.com" \
  --role="roles/storage.objectViewer"

Side note: Don’t “copy-paste” policies—review object/resource-level scoping; wildcard errors here open exposure risk.

5. Infrastructure as Code: Terraform-Driven Cutovers

Refactoring infrastructure provisioning? Maintain parity in Terraform state files for both providers during transition.

Snippet: GCS bucket replacement for S3.

provider "aws" {
  region = "us-east-2"
}

provider "google" {
  project = "acme-prod"
  region  = "us-central1"
}

resource "google_storage_bucket" "static" {
  name     = "acme-static-assets"
  location = "US"
  force_destroy = true
}

# Remove `aws_s3_bucket` from future Terraform runs post-migration

Tip: Always use terraform import on pre-existing resources before rewrite. Otherwise, state drift becomes unmanageable.

6. Testing and Validation

Testing here is non-negotiable. Methods:

• Hash checks or row counts before/after (md5sum, pg_stat_user_tables).
• Smoke tests with controlled test users/data.
• Performance regression: For network-heavy apps, use iperf3 between legacy AWS and new GCP endpoints post-migration.
• Observability: Set up GCP Monitoring/Logging dashboards at least two weeks prior; catch drift and spurious 5xx errors early.

Fake pass is worse than a loud fail—test with representative traffic, not just happy path.

7. Cutover Execution and AWS Decommissioning

Prioritize:

• DNS switch: Use low-TTLs before migration day.
• Progressive decommission: Tag retiring AWS resources with an expiration label and script deletion—avoid zombie spend.
• Post-cutover monitoring: Top offenders—flapping GKE nodes, missing IAM bindings, poor network egress config triggering excessive GCP billing.

Retain AWS backups for at least one retention cycle—don’t trust that you’ll never need reversion.

Cost Optimization: Advanced Moves

• GCP sustained use discounts only apply after steady usage—avoid burst-and-abandon patterns.
• Use bq and gcloud cost forecasts to compare ongoing GCP spend to historical AWS billing; the reality rarely matches calculators.
• GCP egress between regions and AWS<>GCP is expensive: Batch heavy intercloud syncs and prefer regional affinity when possible.

Closing Thoughts

Cloud-to-cloud migration unveils the gaps in documentation, policy, and process. Tools like Terraform and managed migration utilities make transitions tractable, not effortless. Expect to triage permission issues and intermittently, to re-architect “lifted” stacks. The advantage? Not platform-hopping, but building a team culture that treats cloud as substrate—not as destiny.

For tricky edge-cases (e.g. legacy IAM or cross-cloud compliance), proceed incrementally. Focus on validation and observability more than theoretical reversibility.

If you ran into a specific roadblock—possibly an undocumented error or a service limit—document it. Someone else will hit the same wall.