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Cloud

AWS to GCP Data Migration

Led a multi-petabyte data migration from AWS to Google Cloud Platform for an enterprise client, ensuring zero data loss, minimal downtime, and achieving 30% cost reduction.

AWS S3GCP Cloud StorageBigQueryTerraformPythonDataflowStorage Transfer Service

🔄 The Challenge: Cross-Cloud Data Migration

An enterprise client needed to migrate their entire data infrastructure from AWS to Google Cloud Platform – including 50+ terabytes of data across S3, Redshift, and RDS – while maintaining business continuity and ensuring zero data loss.

☁️ AWS (Source)
  • S3 (40+ TB)
  • Redshift (10+ TB)
  • RDS PostgreSQL
  • Lambda Functions
→ Zero Data Loss
🌐 GCP (Target)
  • Cloud Storage
  • BigQuery
  • Cloud SQL
  • Cloud Functions

📋 Migration Strategy

I designed a phased migration approach to minimize risk and ensure business continuity:

1 Assessment & Planning
  • Data inventory and classification
  • Dependency mapping
  • Cost analysis (AWS vs GCP)
  • Risk assessment
  • Timeline and rollback planning
Week 1-2
2 Infrastructure Setup
  • GCP project and IAM setup
  • Network peering (VPN/Interconnect)
  • Target infrastructure provisioning
  • BigQuery datasets and schemas
  • Terraform modules for GCP
Week 3-4
3 Data Migration
  • Storage Transfer Service for S3→GCS
  • BigQuery Data Transfer for Redshift
  • Database Migration Service for RDS
  • Incremental sync for changes
  • Validation checksums
Week 5-8
4 Cutover & Validation
  • Final sync and cutover window
  • Application switching
  • Data validation and reconciliation
  • Performance testing
  • AWS decommissioning
Week 9-10

🔧 Data Transfer Architecture

Object Storage
AWS S3
Storage Transfer Service Parallel transfer, auto-retry
Cloud Storage
Data Warehouse
Redshift
BigQuery Data Transfer Schema conversion, partitioning
BigQuery
Database
RDS PostgreSQL
DMS + pgloader CDC replication, minimal downtime
Cloud SQL

✅ Data Validation Strategy

To ensure zero data loss, I implemented multi-layer validation:

📊 Row Count Validation

Automated comparison of record counts between source and target

🔢 Checksum Verification

MD5/SHA256 checksums for files to verify data integrity

📄 Schema Comparison

Automated schema diff to ensure table structures match

🔍 Sample Data Testing

Random sampling and deep comparison of actual values

📈 Query Result Matching

Running identical queries on both platforms and comparing results

📋 Audit Trail

Complete logging of all transfers, validations, and discrepancies

🤖 Migration Automation

Built Python-based automation to orchestrate the migration:

⚡
Parallel Processing Multi-threaded transfers with configurable workers
🔄
Incremental Sync Only transfer changed files since last sync
⚠️
Error Handling Automatic retry with exponential backoff
📧
Progress Reporting Daily email reports with transfer status

🏆 Migration Results

0 Data Loss 100% data integrity verified
<4h Downtime Final cutover window
50+ TB Data Migrated Across all systems
30% Cost Savings Compared to AWS
⚡
2x Query Performance

BigQuery outperformed Redshift on analytical workloads

📊
Serverless Analytics

No cluster management with BigQuery's serverless model

🔒
Enhanced Security

Column-level security and VPC Service Controls

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