Seamless Data Migration: Efficiently Moving Large Datasets from GCP to AWS

Lifting a multi-terabyte dataset from Google Cloud Platform (GCP) to Amazon Web Services (AWS) is rarely a single-click operation. Massive object buckets, live transactional data, and strict uptime requirements combine to make this a high-stakes exercise for any DevOps or data engineering team.

Why Bother Migrating?

Sometimes, it’s cost. Sometimes, it’s a push for GDPR compliance. More often, teams need AWS-native analytics—think Redshift or Bedrock. Occasionally it’s a straightforward need to avoid single-provider risk.

But theory won’t move petabytes.

The Bottlenecks

• Transfer scale: Wan links choke on big numbers—expect ~50MB/s per thread, far less at sustained peaks unless Direct Connect (AWS) or Dedicated Interconnect (GCP) is in play.
• Zero-downtime demand: You rarely get a global maintenance window.
• Data accuracy: Cross-region and cross-cloud means eventual consistency issues.
• Security policy: Public internet transfers raise compliance reviews; some teams must encrypt-in-transit using customer-managed keys.

Real stumbling block: cold runs reveal far higher egress charges and subtle failures than predicted by marketing docs.

Workflow: End-to-End Example for Cloud Storage → S3 Migration

Scenario: Move 10TB of imagery from GCS to S3, maintaining sub-hour staleness throughout.

1. Inventory & Dry Run

• Use gsutil ls -l gs://bucket and du -sh to validate object counts/sizes.
• Identify long tails: “hot” prefix objects, infrequent but massive archives.

2. Bulk Transfer, Local or Direct

Option A: Disk hop (“brute force”)

gsutil -m rsync -r gs://my-gcp-bucket /data/tmp/      # ~2 Gbps with parallelism
aws s3 sync /data/tmp/ s3://my-target-bucket

Pros: Simple; bash-scriptable.
Cons: Requires double bandwidth and large ephemeral storage; subject to local disk/network reliability.

Option B: Cross-cloud direct

• Enable GCP Storage Transfer Service with AWS S3 as destination.
• For > 100TB, provision AWS Direct Connect and GCP Dedicated Interconnect.
  • Target at least 10Gbps link—throttling is the real enemy here.

Known issue: GCP Storage Transfer Service still enforces some limits on S3 bucket region compatibility and may silently skip objects with legacy ACLs.

3. Handle Incremental Changes (Near-Real-Time Sync)

Bulk copy alone leaves a consistency gap. Two common patterns:

a) Event-based Sync

• Configure GCS Pub/Sub notifications for OBJECT_FINALIZE and OBJECT_DELETE.
• Deploy a Pub/Sub-triggered Cloud Function (or GKE workload) to queue deltas.
• For each event, push object to S3 via boto3 API or AWS CLI.

b) Scheduled rsync

• Hourly gsutil rsync jobs, with --delete flag for full parity.
• Watch for OverQuota errors in logs:

  CommandException: 503 Service Unavailable
  

  Usually a sign to increase parallel thread count, or throttle during cloud-provider maintenance windows.

4. Validate & Monitor

• Use object-level md5 hashes (gsutil hash vs aws s3api head-object ETag) on a random sample.
• For datasets >1M objects, automate compares in batches; don’t trust a single pass.
• Enable CloudWatch and Stackdriver error alerts.

Gotcha: S3 ETags change for multipart uploads. Always use checksum algorithms for binary verification, not raw ETag compare.

5. Cutover & Decommission

• Lower DNS TTL for all endpoints pointing at GCP-backed assets.
• After final delta sync, update clients to S3 URLs. Pause at least 24 hours for monitoring.
• Only then: destroy or archive old buckets. S3 and GCS billing cycles do not align; double-check invoice periods.

BigQuery → Redshift: Tabular Data Pipeline

• Use BigQuery EXPORT DATA (requires Standard SQL) to write Parquet files into GCS:

  EXPORT DATA OPTIONS(
      uri='gs://export-bucket/dt=2024-06-10/table-*.parquet',
      format='PARQUET'
  ) AS
  SELECT * FROM project.dataset.table;
  

• Migrate generated Parquet files to S3 as above.
• Use Redshift’s COPY with FORMAT AS PARQUET:

  COPY schema.table FROM 's3://target-bucket/path'
  IAM_ROLE 'arn:aws:iam::ROLE/RedshiftLoad'
  FORMAT AS PARQUET;
  

  Batch imports improve throughput; watch for Redshift disk temp space exhaustion.

Tip: Watch memory and concurrency limits on Redshift—use wlm_query_slot_count for large imports.

Table: Direct Service Equivalents

Non-obvious Lessons

• For objects >5GB, S3 multipart upload required (catch error: EntityTooLarge in logs).
• Some networks throttle long-running TCP flows after 2-3 hours—prefer chunked, restartable transfers (see: gsutil -m cp -c flag for continue-on-error).
• GCS bucket ACLs map poorly to S3 bucket policies; always audit permissions once complete.

Final Thoughts

No two migrations are identical—network capacity, internal approvals, and service limits will force tactical choices. Still, reliable outcomes come from up-front inventory, staged incremental sync, and brutal validation of data at each step.

Plan for double cloud costs short-term and make peace with a little imperfection—fixes for rare failures should be handled post-cutover during steady state, not under transfer-window stress.

Side note: AWS DataSync now supports GCS as a source, but as of v1.36 (2024-06) real-world throughput is still ~30% below direct S3 PUT via awscli.

Choose tools for your scale, test exhaustively, and don’t trust any single checksum pass—paranoia beats surprise every time.