--- title: Oracle to Snowflake Migration | 2.3B Rows | Zero Data Loss in 10 Weeks description: Agilityx migrated a financial services enterprise from Oracle to Snowflake in 10 weeks. 5.6x faster, zero data loss, $140K annual compute savings. --- [Skip to main content](https://agilityx.ai/results/financial-services-oracle-to-snowflake#main-content) [![Agilityx.ai](https://agilityx.ai/hubfs/raw_assets/public/Agilityx-ai-theme/images/agilityx-logo.svg)](https://agilityx.ai/?hsLang=en) - [Home](https://agilityx.ai/?hsLang=en) - [How We Work](https://agilityx.ai/how-we-work?hsLang=en) - [Services](https://agilityx.ai/services?hsLang=en) - [Results](https://agilityx.ai/results?hsLang=en) - [Partners](https://agilityx.ai/partners?hsLang=en) - [Assessment](https://agilityx.ai/assessment?hsLang=en) - [ROI Calculator](https://agilityx.ai/roi-calculator?hsLang=en) - [About](https://agilityx.ai/about?hsLang=en) [Meet with a Specialist](https://calendly.com/mudasir-agilityx/30min) [Home](https://agilityx.ai/?hsLang=en) [How We Work](https://agilityx.ai/how-we-work?hsLang=en) [Services](https://agilityx.ai/services?hsLang=en) [Results](https://agilityx.ai/results?hsLang=en) [Partners](https://agilityx.ai/partners?hsLang=en) [Assessment](https://agilityx.ai/assessment?hsLang=en) [ROI Calculator](https://agilityx.ai/roi-calculator?hsLang=en) [About](https://agilityx.ai/about?hsLang=en) [Meet with a Specialist](https://calendly.com/mudasir-agilityx/30min) [Back to Results](https://agilityx.ai/results?hsLang=en) Financial Services Data Migration & Cloud Modernization # From Oracle to Snowflake in 10 Weeks — With Zero Data Loss Across 2.3 Billion Rows 10 weeks vs. 14-month original estimate (5.6x faster) Industry Financial Services Service Data Migration & Cloud Modernization Duration 10 weeks Team 4 Agilityx consultants + AI agents Data Volume 2.3 billion rows Oracle Snowflake AWS dbt Python Agilityx AI Agent Suite Meet with a Specialist ## The Situation A Fortune 500 financial services firm had been running its core analytics platform on Oracle for over a decade. The system supported risk modeling, regulatory reporting, and customer analytics across 2.3 billion rows of transactional data. Leadership had committed to a cloud-first strategy, but the migration had already stalled once. The previous vendor — a Big 4 consulting firm — had spent 5 months in discovery and architecture planning alone, consuming $1.2M in fees before a single row of data had moved. The project was projected at 14 months with a 20-person team. The client’s CDO pulled the engagement after the timeline slipped for the third time. The mandate was clear: migrate to Snowflake on AWS, maintain full regulatory compliance, and deliver within one quarter. The internal data engineering team of 6 people could support but not lead the effort. The board had approved budget but was running out of patience. ## The Approach 1 Discovery Week 1–2 2 Architecture Week 2-3 3 Migration Week 3-8 4 Optimization Week 8-10 5 Documentation Continuous 1 ### Discovery (Week 1–2) AI Agents Profiled every table, column, and dependency in the Oracle environment within 48 hours. Identified 340+ data quality issues, 47 undocumented dependencies, and 12 legacy stored procedures needing conversion. Consultants Used AI findings to build a comprehensive migration blueprint in 5 business days. Validated priorities with the client’s data engineering and compliance teams. 2 ### Architecture (Week 2-3) AI Agents Modeled 3 target architecture options in Snowflake, evaluating query performance, cost optimization, and compliance requirements. Consultants Validated the recommended architecture with the client’s security and compliance teams. Finalized the migration strategy. 3 ### Migration (Week 3-8) AI Agents Generated 85% of the migration code automatically, including ORM mapping conversions and stored procedure translations. Ran continuous regression testing, flagging 23 edge cases. Consultants Resolved all 23 edge cases requiring human judgment. Coordinated with the client’s data engineering team. Validated every row against the source — all 2.3 billion. 4 ### Optimization (Week 8-10) AI Agents Optimized query performance, identified $140K in annual compute cost savings through better clustering and warehouse sizing. Established monitoring baselines. Consultants Validated optimization recommendations. Set up operational monitoring and alerting. Conducted knowledge transfer sessions. 5 ### Documentation (Continuous) AI Agents Auto-generated 200+ pages of technical documentation, data dictionaries, and runbooks throughout the engagement. Consultants Reviewed and enriched documentation for accuracy and completeness. Ensured compliance documentation met regulatory standards. ## Traditional vs. Agilityx | Dimension | Traditional | Agilityx | | --- | --- | --- | | Discovery duration | 5 months (incomplete) | 2 weeks (complete) | | Team size | 20 consultants | 4 consultants + AI agents | | Data validation | Sample-based (5% of rows) | 100% — all 2.3 billion rows | | Code generation | Manual (est. 6 months) | 85% AI-generated, human-reviewed | | Documentation | Planned for "post-go-live" | Auto-generated continuously | | Total timeline | 14 months (projected) | 10 weeks (actual) | | Fees before first data move | $1.2M | $0 — data moved in week 3 | Discovery duration Traditional 5 months (incomplete) Agilityx 2 weeks (complete) Team size Traditional 20 consultants Agilityx 4 consultants + AI agents Data validation Traditional Sample-based (5% of rows) Agilityx 100% — all 2.3 billion rows Code generation Traditional Manual (est. 6 months) Agilityx 85% AI-generated, human-reviewed Documentation Traditional Planned for "post-go-live" Agilityx Auto-generated continuously Total timeline Traditional 14 months (projected) Agilityx 10 weeks (actual) Fees before first data move Traditional $1.2M Agilityx $0 — data moved in week 3 ## The Outcomes 10 weeks Total delivery vs. 14-month original estimate — 5.6x faster than the previous vendor projected. 2.3B rows Migrated & validated Zero data loss, zero integrity issues across the entire transactional dataset. 85% AI-generated code Migration code automatically generated by AI agents, then human-reviewed and approved. $140K Annual compute savings Identified through AI-optimized Snowflake warehouse configuration and clustering. 200+ Pages of documentation Auto-generated throughout the engagement, delivered on day one of go-live. 4 people Agilityx team size vs. the 20-person team proposed by the previous Big 4 vendor. "We had already burned over a million dollars with a Big 4 firm that couldn’t get past the planning phase. Agilityx had data moving within three weeks. Their AI agents caught data quality issues across 2.3 billion rows that the previous team missed entirely. This is what consulting should look like in 2026." Chief Data Officer Fortune 500 Financial Services Firm ### Project Details Industry Financial Services Service Data Migration & Cloud Modernization Duration 10 weeks Team 4 Agilityx consultants + AI agents Data Volume 2.3 billion rows Technologies Oracle Snowflake AWS dbt Python Agilityx AI Agent Suite Meet with a Specialist ## Facing a similar challenge? Let's talk. Book a 30-minute discovery call and let's discuss how the Build With model can work for your organization. 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