From Oracle to Snowflake in 10 Weeks — With Zero Data Loss Across 2.3 Billion Rows
Industry
Financial Services
Service
Data Migration & Cloud Modernization
Duration
10 weeks
Team
4 Agilityx consultants + AI agents
Data Volume
2.3 billion rows
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
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.
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.
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.
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.
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
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