Migrate From Aws To Google Cloud

Strategic Migration from AWS to Google Cloud: Engineering Guide for Minimal Downtime

Migrating production infrastructure between major cloud vendors is rarely plug-and-play. On paper, AWS and Google Cloud both advertise feature parity—compute, managed databases, storage, IAM. In reality, implementation details diverge, and those gaps surface during critical cutover phases. Cut corners, and you’ll either overpay or face outages. Below: practical workflow, potential pitfalls, and advice for minimizing risk while making the switch.

Baseline: Inventory, Dependencies, and Architecture

Begin with what you actually run. Not everything should move.

• Asset audit, not wishful guesswork. Pull live data. aws ec2 describe-instances, aws rds describe-db-instances, S3 inventory reports, IAM role exports. For large estates: aggregate results with scripts.
• Dependency mapping. Static application dependencies, egress/ingress patterns, inter-service data flows. Diagram service chains, or better, extract from CI/CD definitions (e.g., Github Actions, Jenkinsfiles).
• Example: Django 4.2 (Python 3.10), Gunicorn behind ALB, workers via Celery, PostgreSQL RDS (v13), Redis ElastiCache, private S3 storage. Pulumi or Terraform IaC defines networking—VPCs with NACLs, peering to legacy data center.

Caveat: Don’t trust CMDBs or static docs—they’re stale or incomplete by default.

Mapping: Not All Services Translate 1:1

Column A isn’t always plug-compatible with Column B. Here’s where shape mismatches cause friction:

• IAM policies do not directly map. GCP’s resource hierarchy (organizations, folders, projects) adds another layer. Prepare to refactor policies.
• Networking: Subnet management, private service access, and shared VPC are handled differently; GCP’s global VPC routing isn’t available in AWS.

Architectural Refactor: Optimize for Google, Don’t Mimic AWS

Treat migration as license to modernize. E.g.:

• EC2 fleets: Rarely efficient to 1:1 re-host. Where appropriate, containerize to GKE, manage via Helm (v3+), and shift to horizontal auto-scaling.
• Legacy RDS: Opt for Cloud SQL for straightforward requirements. For multi-region/federated workloads, evaluate Spanner even with higher complexity/cost.
• Stateful storage: Cloud Storage bucket layout and lifecycle policies differ subtly from S3—test signed URLs, object versioning, and Class A/B operation costs.
• Security: Rebuild IAM least-privilege roles; don’t “lift and shift” broad admin policies.

Data Migration: Throughput, Integrity, and Downtime Windows

Bandwidth, consistency, and data gravity matter most.

• Bulk object transfer: Use Storage Transfer Service:

  gcloud transfer jobs create --source-awss3-bucket=prod-assets --destination-bucket=gs://prod-assets-gcp \
    --source-awss3-access-key=AKIA... --source-awss3-secret-access-key=xxxx
  

  (Set up AWS S3 “read only” IAM policy for transfer jobs.)
• Database movement: For PostgreSQL, consider:
  • pg_dump/pg_restore for basic export/import. Accepts downtime window.
  • Native logical replication (Postgres >=10) for zero-downtime cutover. Example error during slot setup to watch for:

    ERROR:  replication slot "gcp_migration" already exists
    

  • Replication lag metrics (pg_stat_replication) must be closely monitored during dual-write windows.
• Pitfall: Egress charges from AWS are non-trivial at scale; for petabyte-class transfers, negotiate with your AWS account manager or consider AWS Snowball to GCP Transfer Appliance handoff.

Data integrity: Always validate record counts, checksums, and—if possible—application-level invariants both pre- and post-move.

Networking and IAM: Parity ≠ Compliance

• VPC: Define subnets with non-overlapping CIDR blocks. Enable flow logs early for troubleshooting. Emulate AWS Security Groups with equivalent GCP firewall rules, but note the lack of stateful resources in GCP firewall logic.
• DNS Cutover: Lower TTLs at least 48h prior. Use DNS health checks or traffic manager for progressive rollout (weighted records or split-brain if necessary).
• Service Accounts/IAM: Map minimum-privilege IAM via GCP “principals” (users, groups, service accounts). Replica roles must be tested with gcloud iam list-grantable-roles.

Incremental Migration: No “Big Bang” Deploys

• Canary approach: Migrate non-critical or stateless services first; maintain dual-running for at least a full release cycle.
• Synchronization lag: For S3 to Cloud Storage, run gsutil rsync nightly until final switchover.
• Database cutover: Use logical replication to shadow-write to Cloud SQL, promote GCP primary after last successful sync. Accept that there may be seconds-to-minutes of write downtime depending on replication lag buffer—communicate to stakeholders.
• Known issue: GKE clusters may have imagePull backoff errors if using custom registries—allowlist IPs and pre-pull images ahead of migration day.

Finalization: Optimize, Monitor, and Decommission

• Autoscaling: Adjust Compute Engine/Node Pool settings. Initial over-provisioning is cheaper than downtime.
• Cost analysis: Use GCP Billing export to BigQuery for granular cost breakdown—unexpected spikes often appear in network egress or BigQuery queries.
• Cloud-native enhancements: Run GCP Security Command Center, configure Stackdriver Monitoring alerts and log sinks for post-migration observability.
• Backup regime: Don’t port AWS backup schedules blindly; configure GCP-native snapshot/backup according to new SLAs.

Side note: GC resource labels are invaluable for ongoing cost tracking and resource governance. Tag everything.

Recap (Punchlist Form)

• Audit current estate — trust live data, not spreadsheets.
• Map each AWS resource to a GCP equivalent, considering trade-offs and gaps.
• Redesign for platform strengths (GKE, Cloud Spanner)—don’t just replicate weak old patterns.
• Test and validate all data migration, especially across stateful systems.
• Build IAM and network configuration new; refactoring is safer than mirroring.
• Migrate by phases, monitor each move, and only decommission AWS after thorough validation.

Non-Obvious Tip

When using hybrid cloud (transitional) states, leverage GCP Interconnect or VPN to minimize latency between clouds, but benchmark actual throughput—numbers often differ substantially from vendor “maximums”.

Cloud-to-cloud migration is less about tools and more about operational discipline and readiness to exploit the new platform’s strengths. Debug as you go, document every exception, and don’t expect perfection on day one.

If you spot GCP-specific runtime quirks—such as differing semantics in IAM propagation or GKE pod scheduling—capture these in runbooks; they’ll be reference points for every future migration.

Questions on specific architectures, multi-cloud scenarios, or pitfalls? Reach out.