Migrate From Gcp To Aws

Strategic Roadmap: GCP ➞ AWS Migration with Minimal Downtime & Spend

Cloud migrations are rarely straightforward, especially between hyperscalers. The reality: mismatched primitives, subtle service differences, and the specter of downtime. Rushed execution here means ballooning costs and outages—worse if you touch data pipelines or stateful workloads.

Problem Statement

A SaaS analytics platform running reports nightly hits escalating costs on Google Cloud. Finance mandates a move to AWS—no more than two hours downtime. Core components: GKE for microservices, BigQuery for warehousing, Cloud Functions for scheduled ETL. Challenge: No loss of streaming data, same end-user DNS, and tight timeline.

1. Inventory & Evaluate GCP Dependencies

Don’t rely on IAM console exports. Use the gcloud asset export command:

gcloud asset export --content-type=resource \
  --output-path=gs://<BUCKET>/inventory.json \
  --project=<PROJECT_ID>

Parse this exhaustively. Identify:

• GKE clusters (note versions; e.g., 1.26.x to match EKS possible node OS/distros)
• BigQuery datasets (size, region, update frequency)
• Pub/Sub topics and triggers
• Firewalls, VPCs, custom routes (for zero-trust or interconnect)
• Service accounts, KMS keys

Skills gap shows here: custom plugins or marketplace images must be noted for translation or replacement.

2. Service Mapping: GCP ➞ AWS

No 1:1 mapping exists. Example table:

Beware: BigQuery’s flat-rate pricing doesn’t parallel Redshift’s instance or on-demand pricing. Precompute queries’ new costs using the AWS Pricing Calculator.

3. Data Migration: Bottlenecks and Incremental Sync

BigQuery datasets >2TB are routine in analytics orgs.

Options:

• AWS Snowball Edge: Physically transfer petabyte-scale data; encrypts at rest and in transit.
• Custom ETL Pipelines: Use Apache Beam or Airflow to extract-incremental in batches (e.g., last-modified timestamp). Airflow DAGs can orchestrate between bq extract and aws s3 cp.

Example Airflow snippet (PythonOperator):

extract = BashOperator(
    task_id="extract_bq",
    bash_command="bq extract --destination_format=CSV ...",
    dag=dag
)
upload = BashOperator(
    task_id="upload_s3",
    bash_command="aws s3 cp ...",
    dag=dag
)

Gotcha: BigQuery TYPEs (e.g., ARRAY, STRUCT) will need conversion logic. Do not expect numeric precision/scale to transfer natively.

For streaming pipelines: Implement dual-writes during cutover—Kafka Connect and Kinesis Data Firehose both support this, but partition mapping may differ.

4. Service Interlocks: Transition Without Breaking Chains

Integrated event-driven architectures are brittle during migration. Consider this case:

• GCP Cloud Functions process files on GCS.
• Target: AWS Lambda processing S3 ObjectCreated events.

Steps:

1. Deploy Lambda functions, connect to S3 event notifications.
2. Set up batch sync (e.g., gsutil rsync with aws s3 sync) for new files.
3. Parallel run for >24 hrs, monitor logs for lost/duplicate events.

Error to expect:

[ERROR] KeyError: 'Records' - common if S3 event format changes

Handle with robust input validation.

Note: If latency increases on AWS, assess CloudWatch metrics; VPC endpoint misconfiguration can cause +100–300ms per event.

5. Cost Controls & Resource Rightsizing

Historical GCP utilization guides AWS capacity planning:

• Run gcloud beta compute instances list --format="..." for 30-day CPU/mem metrics.
• Use AWS Compute Optimizer recommendations post-PoC; reject defaults blindly—performance profiles differ (e.g., c6g instances on AWS ARM vs. GCP n2d AMD).

Implement:

• Savings Plans for predictable users
• Instance families: If migrating Java microservices, don’t use t3.medium for stateful workloads; go r5 or m6i.
• Enable S3 Intelligent-Tiering early. Most forget—accumulated logs can bankrupt month two.

6. Automated End-to-End Validation

Test everything, or deal with midnight alerts post-cutover.

• Use data checksums: md5sum pre- and post-migration, audit 0.01% as a SRE baseline.
• For service validation, consider ephemeral test harnesses (e.g., Terratest in Go).

Edge case: Certain API Gateway/Lambda combos have 30s client timeouts—different from Cloud Endpoints.

Stress test with tools like k6:

k6 run -u 1000 -d 5m script.js

If latency >20% increases, re-trace between NLBs and compute.

7. Cutover: Downtime, DNS, and Rollback

Plan for immutable DNS (Route 53 and Cloud DNS TTL at 60s pre-cut). Monitor for stale cache.

Maintain:

• Last-known-good GCP snapshot.
• Automated failback script (terraform workspace select gcp-live && terraform apply) as a parachute.

Avoid flipping until smoke tests turn green under synthetic and real loads. Don’t trust console “Completed” messages—check via CLI and direct application probes.

Side Strategies & Gotchas

• Iac Parity: Use Terraform v1.5+ with provider blocks for both clouds; track state separately to avoid surprise drifts.
• Unified Monitoring: Run Datadog or Grafana Tempo agents in both clouds—even during the overlap window.
• Secret Management: Transitioning from GCP KMS to AWS KMS isn’t transparent. Re-encrypt secrets before redeploy.

Known issue: IAM role mappings can break if account e-mail conventions differ. Reconcile users/SAML providers in advance.

Summary

Precision in mapping, staged data sync, and robust service cutover are what reduce risk—no shortcut. Spend time testing, automate war-room scenarios, and track not just “works” but “costs” pre- and post-migration. Alternative tools (Velero, CloudEndure, DMS) available, but be wary of their quirks.

Not everything will port perfectly. Sometimes rearchitecting is lower risk than “migrate as-is.” And every migration leaves a few loose ends—just document each one.

Real-world migration pain points or approaches that worked better? Log specifics, not just successes. Engineers will thank you next cycle.