A Data Leader's Guide to Evolving Your Data Lakehouse for AI

Data leaders are navigating three forces simultaneously: explosive growth in unstructured data volume and velocity; rapid technical evolution of lakehouse components, particularly open table formats and object storage; and expanding AI stack requirements that raise the bar for performance, governance, and data quality. This guide clarifies the distinctions among data marts, data warehouses, and data lakehouses, reinforces core architecture concepts, and focuses attention on three high-impact areas where these forces create both risk and opportunity. It provides practical guidance on evaluating open table format options, including when to adopt Apache Iceberg versus alternatives, making the right object storage decisions for AI workloads, and adapting lakehouse architecture as requirements continue to shift. Structured as a decision framework rather than a product guide.