Oracle 19c to Amazon Aurora PostgreSQL — 14TB Migration in 3 Days

The Challenge

This Tier-1 fintech processes over 2 million transactions daily across its core banking platform — a 14TB Oracle 19c database handling payments, settlements, account management, and fraud detection. The CTO had mandated a move to Amazon Aurora PostgreSQL to reduce licensing costs and improve cloud-native scalability.

The constraints were non-negotiable: zero tolerance for data loss, no maintenance window during trading hours (6am–10pm IST), and a requirement to maintain full PCI-DSS and SOX compliance throughout.

The Dflux.ai Approach

Dflux.ai's AI agents began with a full schema analysis of all 340 tables, identifying 23 type conflicts (primarily DATE/TIMESTAMP timezone handling and Oracle NUMBER precision mappings), 8 stored procedures requiring PostgreSQL equivalents, and 4 partitioned tables needing strategy changes.

• Day 1: Schema analysis, AI-generated mappings, customer review and approval
• Day 2: Full backfill of 14TB via parallel read streams, dual-write CDC sync initiated
• Day 3: Sync lag reduced to <50ms, final validation, 4-second cutover at 2am IST

Results

Post-migration validation confirmed 100% row count match across all 340 tables, column-level checksum verification, and statistical distribution sampling showing no precision loss or data transformation errors. The Aurora cluster immediately showed a 23% improvement in query latency due to improved indexing strategy applied during migration.

What's Next

Following the success of the core banking migration, the same client is now using Dflux.ai to migrate their fraud detection data warehouse (2.8TB, Teradata → Snowflake) and their analytics replica (PostgreSQL → BigQuery). Dflux.ai now manages their full data infrastructure migration programme.