Airbnb's expansion into Homes, Experiences, and Services necessitated a revamped data modeling framework for their offline data warehouse. The core challenge was balancing consistency with flexibility to avoid data silos and technical debt. They opted for a balanced approach combining centralized principles with decentralized modeling guidelines. Three foundational principles ensured consistency: no hybrid models, consistent identifier naming, and clear namespace organization. Modeling guidelines empowered teams to choose between separate or monolithic models based on shared vs. unique attributes, future evolution, and downstream consumers. Product-specific domains like Listings, Availability, Location, and Guest interactions opted for separate models due to distinct attributes. Conversely, cross-cutting concepts like Messaging, Payments, and Customer Support benefited from a monolithic model for a unified view. The offline data warehouse acts as a crucial translation layer, standardizing raw production data for analytics. Managing data debt, including migrating legacy tables, was a significant undertaking requiring careful communication and validation. This framework provided a scalable and consistent foundation for Airbnb's evolving data needs.