What Is A Data Lakehouse? Architecture And Trade-offs

This post details the architecture and trade-offs of data lakehouses, focusing on open table formats (Iceberg, Delta Lake, Hudi) and their role in unifying data lakes and data warehouses. It explains how lakehouses enable separate compute and storage, support batch and streaming workloads, and facilitate diverse data types for analytics and ML. The post also highlights that a lakehouse alone doesn't fix analytics workflows, emphasizing the need for a robust analytics layer with a context layer for AI grounding and governance that works for both humans and AI. It discusses the importance of semantic models and observability in the context layer and advocates for centralized policy enforcement and audit trails for governance.