Step-by-Step Guide to Seamless Migration from AWS to GCP with Minimal Downtime

Migrating workloads from AWS to Google Cloud Platform (GCP) is increasingly common as organizations seek to optimize cost structures, access Google’s data-driven toolchain, or diversify cloud deployments. Getting it right requires more than just replicating infrastructure—a successful migration demands detailed inventory, compatibility mapping, and staged execution. Below, a field-tested framework for minimizing disruption.

Drivers for AWS-to-GCP Migration

• Pricing Leverage: GCP offers sustained-use discounts and custom VM types; pricing can drop 15–35% for certain workloads compared to AWS, especially under committed use.
• Advanced Analytics: Vertex AI, BigQuery, and Dataflow integrate cleanly with GCP’s stack—useful for teams already leveraging Google’s machine learning APIs or massive-scale analytics.
• Multi-Cloud & Risk Management: Running critical workloads across providers has become mandatory for many to satisfy compliance, uptime, or vendor diversification requirements.
• Global Network: Google’s backbone and edge POPs, along with features such as global load balancing, simplify serving users at scale.

Phase 1: Assessment & Planning

Inventory Existing Resources

Start with a comprehensive export of AWS resource inventories. Use aws resourcegroupstaggingapi get-resources or aws ec2 describe-instances (for EC2) for insight. Organize the inventory by workload criticality and interdependencies. Example structure (omit trivial services):

Service Mapping

GCP equivalent services rarely match one-for-one. Build an explicit mapping, noting potential feature gaps:

Gap & Refactoring Analysis

Identify non-portable components early. For example, S3 event notifications to Lambda require translation to Pub/Sub triggers in GCP—simple “rsync” isn’t enough.

If your Python app uses boto3 against S3, plan to refactor with google-cloud-storage. Also, Cloud SQL does not support every MySQL/MariaDB/Postgres system variable—run SHOW VARIABLES and cross-compare. Known gotcha: Cloud SQL limits max connections differently than RDS.

Define Success Metrics

Document accepted downtime (e.g., ≤5min), RPO/RTO, data validation methods (CHECKSUM TABLE for MySQL, MD5 for object data), and rollback steps—if rollbacks aren’t possible, state so explicitly.

Phase 2: GCP Foundations

Environment Initialization

• GCP Project creation (use gcloud projects create—avoid reusing test projects for prod).
• IAM: Define custom roles before importing users. Use service accounts with minimal permissions rather than primitive "Editor" roles.
• Networking: Map CIDR ranges; overlapping subnets can break later VPN peering. Take time now to set up VPC, subnets, firewall rules, private Google access, and Shared VPC if needed.
• Logging/Monitoring: Integrate Cloud Logging and Monitoring before migration to detect early problems (alternatively, export to external SIEM/Splunk).

Phase 3: Data & Object Migration

Database Migration

For MySQL/Postgres:

• Use Google Database Migration Service (DMS). Version as of 2024: DMS supports transactional sync with minimal downtime for MySQL 8.0, Postgres 15, SQL Server 2019.
• Caveat: Ensure source RDS uses binlog/replication compatible settings (binlog_format = ROW).
• Replication lag can spike during large updates. Monitor via Stackdriver and SHOW SLAVE STATUS equivalent in Cloud SQL.
• Example cutover:

-- On source (AWS RDS)
FLUSH TABLES WITH READ LOCK;
SHOW MASTER STATUS;

-- On GCP, ensure Cloud SQL DMS replication status is 'replicating'
-- Cutover timing:
SET GLOBAL read_only = ON;  -- minimize drift window
... stop writes, perform validation ...

For Redis/Memcached: No native equivalent migration tool—scripted dump/restore or invest in Redis Enterprise (multi-cloud support) if zero-downtime is essential.

Blob/Object Storage

Use GCP Storage Transfer Service to schedule initial sync, then incremental. For low downtime, avoid one-time big pushes—repeat delta sync up to cutover.

gsutil -m rsync -d -r s3://source-bucket gs://dest-bucket  # note: '-d' deletes extraneous files

Watch out for S3 ACLs vs GCP IAM; not all permissions transfer directly.

Phase 4: Application Migration

VM-Based Workloads

Import EC2 AMIs via GCP’s VM Import/Export. Note: Image conversion may fail with custom kernels—stick with Amazon Linux 2 or CentOS 7. Decompose large databases from pre-packaged VMs.

For Kubernetes:

• Export Kubernetes YAML manifests (kubectl get all --export deprecated, use kubectl get ... -o yaml) and migrate to GKE.
• Validate volume plugins; AWS EBS won’t port—use GCP Persistent Disks. Helm chart rewrites may be required if storageClassName is hard-coded.

Application Config and Endpoints

Explicitly update endpoint DNS, config files, environment variables. In CI/CD (e.g., GitHub Actions, Jenkins), create separate deployment pipelines per environment to compartmentalize risks.

Phase 5: Validation & Cutover

End-to-End Validation

• Validate data integrity (application-level tests, SQL checksums, API contract tests).
• Performance/load testing: Use Locust, JMeter; for critical workloads, run both clouds in parallel and mirror traffic to GCP for a smoke test period.
• Observability: Deploy Stackdriver agents before cutover; compare log patterns and error rates week-over-week.

Partial cutovers (canary style) using weighted DNS are more robust than full “big bang” swaps. Example DNS TTL reduction table:

Key Post-Migration Actions

• Monitor error rates, queue lag, and DB replication. Critically: latency spikes often surface 1–3 hours after traffic switch due to cold starts or regional cache misses.
• Rollback: Keep AWS data in sync post-cutover for at least 48h (if feasible); only decommission AWS resources after multiple validation cycles.
• Update backup regimes—do not assume GCP snapshots are enabled by default.

Known Issue: Cloud Storage object versioning and S3 versioning are not identical; test archival/reversion flows if regulatory requirements exist.

Bonus: Practical Tips

• IaC everywhere: Use Terraform (>=v1.4.0 for improved Google provider state import) to create GCP infra. Store state in a GCS bucket before any prod launches.
• Audit everything: Use both AWS CloudTrail and GCP Audit Logs during migration window.
• Don't trust default quotas: GCP project quotas (e.g., IP addresses, CPUs per region) are low initially—file quota increase requests minimum 1 week in advance.
• Use labels and tagging: Bulk resource management is much easier with consistent labels, especially during dual-cloud operation.

Migrating from AWS to GCP is fundamentally a translation problem—each service, each access pattern carries its own quirks. By segmenting inventory, running multiple validation passes, and keeping operational eyes on both platforms during cutover and stabilization, you can avoid common pitfalls and reduce downtime to minutes, or even seconds. There will always be edge cases (for example, EMR pipelines with hardcoded S3 targets, or IAM policy constructs that don’t map); plan to address these iteratively. Consider this sequence a flexible template, not gospel.

Ready to start? Begin with a meticulous inventory—getting that part wrong will cost you later.