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A Fortune 500 enterprise running 12 years of mission-critical analytics on a legacy on-premise data warehouse faced a stalled cloud migration. Engineering teams had cataloged 4,800+ ETL pipelines, 11,000+ stored procedures, and 320+ TB of historical data targeted for Snowflake and Databricks.
However, manual rewrite estimates extended delivery timelines to 18+ months with a projected $42M services cost, making the program economically and operationally unviable.
Migration debt was blocking three GenAI initiatives waiting on a governed cloud foundation. With shareholder commitments tied to fiscal-year cost-takeout targets, the organization required a scalable approach to refactor legacy SQL, regenerate transformation logic, and validate output parity — without increasing headcount or extending the timeline.
Novacis Digital leveraged its purpose-built Data Modernization AI Solution to compress migration timelines through LLM-powered code generation and governed transformation workflows.
The AI solution ingests legacy SQL, stored procedures, and ETL definitions, automatically generating Snowflake- and Databricks-native code with embedded schema mapping, data-quality remediation, and lineage documentation produced in a single pass.
Context-aware AI agents read source dialects including Teradata, Oracle, and SQL Server, decode embedded business logic, and generate production-ready target code—shifting engineering effort from manual development to validation and optimization.
Automated parity testing compares source and target outputs at a row level prior to cutover, ensuring data consistency and minimizing migration risk. Human-in-the-loop validation checkpoints route low-confidence transformations to senior engineers, maintaining strict governance over critical financial and customer data domains while enabling high-volume pipeline automation.
Migration Capabilities
Solution Features
The platform was deployed alongside the client's migration program in 4 weeks, with the first 600 pipelines refactored and validated in the next 8 weeks — generating early cost-takeout savings that funded the subsequent migration phases.
Migration milestones tracked ahead of plan from the first sprint through final cutover, with governance and lineage compliance validated by internal audit prior to each release cycle.
$28M services cost avoidance, delivering immediate ROI versus manual migration.
67% reduction in migration timeline (18 months → 6 months), accelerating cloud adoption.
3 GenAI initiatives unblocked 12 months earlier, unlocking strategic AI investments.
4,800+ ETL pipelines refactored at 99.4% output parity.
320+ TB of historical data migrated with full lineage traceability.
