Challenge

Legacy on-premises data infrastructure running Oracle OLTP systems and Teradata data warehouse nearing end-of-life. Maintenance costs escalating 25% annually. Systems lacked scalability for growing data volumes; query performance degrading. No cloud strategy in place; business blocked on modern analytics and ML initiatives.

Approach

Strategic Plan:

• Phased migration approach (3 workstreams operating in parallel: transactions → analytics → ML datasets)
• Hybrid cloud period (6 months) with dual-write capability for safety net
• Zero-downtime cutover strategy using application-level routing and CDC (Change Data Capture)
• Separate cloud destinations: Snowflake for analytics/BI, BigQuery for ML/real-time workloads

Technical Leadership:

• Architected data replication pipelines using Attunity for Oracle/Teradata CDC
• Designed schema migration strategy accounting for dialect differences (SQL variants)
• Implemented data validation framework: row counts, checksums, reconciliation queries
• Established cost monitoring and governance in cloud environments
• Built fallback procedures for rapid rollback if needed

Team

• Size: 14 people (1 PM, 2 data architects, 4 cloud engineers, 3 DBAs, 2 data analysts, 1 security/compliance, 1 finance)
• Duration: 9 months
• Cross-functional: Finance, IT, Security, Lines of Business

Results

Delivery Metrics

• On-time: ✓ Completed in 9 months (planned 10 months)
• Budget: $3.2M actual vs $3.1M planned (+3% variance)
• Scope: 100% - All critical systems migrated; legacy systems decommissioned
• Zero-downtime cutover: ✓ All workstreams cut over within 4-hour maintenance window

Technical Impact

• Data migration volume: 847 GB Oracle + 1.2 TB Teradata → Snowflake + BigQuery
• Query performance: Improved 3-5x for common analytical queries
• Data freshness: Real-time replication (Snowflake) and streaming (BigQuery)
• System reliability: 99.98% uptime vs 96% on-prem availability
• Migration validation: 100% row count match within 0.001%, zero data loss

Business Impact

• Infrastructure cost: $2.1M/year → $840K/year (60% reduction = $1.26M savings)
• Deferred capex: Saved $4M in hardware/licensing renewal costs
• Time-to-value: Analytics queries now self-service in BigQuery; no longer dependent on team
• Unlocked capabilities: 5 new ML models and real-time dashboards now possible
• Team productivity: DBAs transitioned from 80% infrastructure work to 100% analytics engineering

Key Decisions

1. Separate cloud destinations - Snowflake for historical analytics (cost/performance), BigQuery for ML workloads (speed/integration)
2. Hybrid cloud period - 6-month overlap reduced risk and gave teams time to validate cloud behavior
3. CDC-based replication - Enabled live migration without application changes
4. Aggressive timeline - Compressed from 15 months to 9 months; executive commitment on resources made it possible