Explores the gap between AI agent demos and production-ready enterprise systems, focusing on data and knowledge challenges.
Explores combining Apache Iceberg REST Catalogs and Arrow Flight for high-performance columnar data transfers in lakehouse architectures.
Explores the Dremio Agentic Lakehouse concept: data built for AI agents and platform management automated by agents, with architecture patterns and production considerations.
Explains why AI agents need a context layer with lineage, quality, freshness, and ownership for reliable analytics.
Analysis of how enterprise AI value comes from governed context and data contracts, not just model choice.
Explains the four-layer architecture of an agentic lakehouse for reliable AI agent data access.
A step-by-step playbook for migrating from legacy data warehouses to open lakehouses, covering inventory, architecture, and trust-building.
