Fortune 500 manufacturing giant saves estimated 9 months on Snowflake + dbt migration with AI-powered automation
A Fortune 500 manufacturer used Datafold’s AI-powered Migration Agent to migrate 400+ legacy stored procedures from Microsoft Synapse to Snowflake + dbt Core—cutting an expected 12-month project down to just 3 months. Instead of relying on consultants, they used Datafold’s AI-powered Migration Agent to automate code translation and validate data parity. In just 3 months, they reached user acceptance testing—far ahead of schedule—while eliminating vendor costs and preserving data accuracy. Datafold’s tooling even decomposed a 300,000-character stored procedure into 400 dbt models, turning a high-risk migration into a repeatable, AI-driven playbook for future acquisitions.
Introduction
A Fortune 500 manufacturer used Datafold’s AI-powered Migration Agent to migrate 400+ legacy stored procedures from Microsoft Synapse to Snowflake + dbt Core—cutting an expected 12-month project down to just 3 months. Instead of relying on consultants, they used Datafold’s AI-powered Migration Agent to automate code translation and validate data parity. In just 3 months, they reached user acceptance testing—far ahead of schedule—while eliminating vendor costs and preserving data accuracy. Datafold’s tooling even decomposed a 300,000-character stored procedure into 400 dbt models, turning a high-risk migration into a repeatable, AI-driven playbook for future acquisitions.
The challenge: A complex acquisition integration
When the company acquired a large life sciences manufacturing firm—one of their biggest acquisitions to date—they inherited a sophisticated data warehouse environment running on Microsoft Synapse with extensive Power BI reporting. Unlike smaller acquisitions, this company had a fully developed data infrastructure that business users depended on daily.
The legacy environment was maintained entirely by a third-party vendor at significant annual cost. The company faced a critical decision: continue paying hundreds of thousands of dollars annually for external maintenance, or migrate the complex system to their modern Snowflake and dbt Core environment.
The project involved migrating over 400 stored procedures from the legacy Synapse environment. Based on previous migration experience, the team knew the risks involved. A recent Oracle Business Intelligence to Snowflake migration had taken 18 months instead of the planned 12 months, with costs running 30% over budget.
The fundamental problem with legacy code developed over 10-15 years is that institutional knowledge disappears, making these systems essentially black boxes. Traditional consultants would require deep expertise in the legacy environment, but even with experts, such complexity could take a full year to manually refactor and validate.
The solution: Datafold Migration Agent
As an existing Datafold customer using the platform for data quality workflows, the company was intrigued when they learned about Datafold's AI-powered Migration Agent (DMA). DMA combines advanced LLM technology for automated code translation with Datafold's proprietary data diffing capabilities to ensure perfect data parity between legacy and new systems. The timing aligned well with their organization's broader embrace of AI technologies.
The project leader emphasized a key advantage: Datafold's team didn't need to be experts in Microsoft Synapse or the specific stored procedures. Traditional approaches require consultants to first become experts in the legacy system—a time-consuming and risky process.
The Migration Agent's built-in data validation capabilities were particularly compelling, automatically validating that outputs matched perfectly between legacy and new environments while providing auditable proof of accuracy.
The results: Transformational migration success
The migration was completed in dramatically less time than traditional approaches would have required, while maintaining perfect data accuracy and eliminating hundreds of thousands of dollars in annual third-party maintenance costs.
Unprecedented speed and cost savings
In just three months, the company has reached the user acceptance testing phase—a milestone the project leader described as unheard of for a migration of this scale and complexity. The successful migration enabled them to terminate their expensive third-party vendor contract while bringing critical business capabilities in-house.
Stakeholder confidence through validation
The project built significant trust with stakeholders from the acquired company who became convinced when they saw comprehensive data validation proving accuracy. The ability to provide links to detailed data diffs showing exact validation results gave stakeholders confidence in the migration's success.
Conquering the impossible with AI precision
The migration's most dramatic technical success involved what the Datafold team dubbed "The Kraken"—a single stored procedure containing 300,000 characters of code, larger than many novels. Datafold's "Slicer" technology intelligently broke down this massive sproc into approximately 400 individual dbt models, essentially creating a medium-sized dbt project from one legacy procedure. The project leader noted that without AI and data diff validation, they couldn't have imagined completing such complex refactoring in the delivered timeframe.
Established a scalable competitive advantage
The success opened doors for future acquisitions, giving them confidence that they can integrate complex data environments quickly and cost-effectively. They've already introduced Datafold to other leadership teams evaluating similar projects, recognizing that AI-powered migration tools can accelerate their aggressive acquisition timeline while maintaining quality standards.
When showcasing their modern data stack to teams across their global organization, colleagues are asking when they can access the same capabilities, driving further democratization of their advanced data platform.