Industrializing Large-Scale Historical Data Migration to the Lakehouse

Migrating large-scale historical data to a Lakehouse platform presents significant challenges in schema consistency, data integrity, and auditability, especially at multi-petabyte scale. This paper outlines a framework-driven approach that leverages inventory-based orchestration, automated schema standardization, parallelized ingestion, and structured remediation to enable controlled and scalable migration.

By incorporating multi-layer reconciliation and real-time observability, the approach ensures end-to-end data validation and stakeholder confidence. The result is a repeatable, enterprise-grade migration methodology that enables organizations to efficiently modernize legacy data platforms while establishing a trusted foundation for analytics and AI.