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, lineage, and auditability, particularly at multi-petabyte scale and within highly regulated Financial Services and Insurance (FSI) environments. 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, end-to-end traceability, and real-time observability, the approach supports the stringent data quality, regulatory reporting, risk management, and audit requirements common across banking, capital markets, payments, and insurance. The result is a repeatable, enterprise-grade migration methodology that enables FSI organizations to modernize legacy data platforms efficiently while establishing a governed, trusted foundation for analytics, AI, and regulatory workloads.