Why Data Products and Lakehouses Work Better Together

Data products and data lakehouses are two complementary components that together address the complexities of modern data management, particularly in large organizations with distributed data systems. Data products apply a customer-centric approach to data, emphasizing discoverability, governance, and ease of use, while data lakehouses offer a technical foundation that combines the flexibility of data lakes with the performance of data warehouses. This combination allows for federated data access without requiring all data to be centralized, thus preserving data locality and enabling efficient data consumption across various domains. Key technical enablers like Apache Iceberg and Trino facilitate schema evolution, query federation, and integration between batch and streaming data, ensuring that data products remain adaptable and accessible. These capabilities are particularly beneficial for AI and machine learning workloads, which require context-rich data and robust governance to meet regulatory standards. By enabling distributed but coordinated governance, data products and lakehouses allow organizations to manage data efficiently across multiple systems and regions, reducing data movement costs and enhancing data freshness. This architecture is increasingly adopted across industries like financial services, healthcare, and government for applications ranging from risk management to secure data sharing, offering a scalable and unified approach to data strategy that supports both current analytics and emerging AI needs.