quangnovartec

In the AI era, data warehouse migration is a critical necessity, as legacy systems hinder innovation. A successful migration requires a "Re-architect" or "Hybrid" strategy. This guide outlines a framework for these projects, starting with a crucial Assessment & Planning phase. This includes analyzing query logs and building a solid business case. Next is Design & Modernization, where you create a new cloud-native blueprint, optimizing performance with clustering keys. The core Execution phase involves data migration and rigorous validation to ensure data integrity. Post-migration, the focus is on Re-engineering Analytics, adopting the ELT philosophy, and implementing FinOps for cost management. This ensures your data infrastructure is ready for future growth and AI demands.