Iceberg v3 row lineage adds _row_id and _last_updated_sequence_number to every table, enabling native change data capture without Debezium or Kafka.
Iceberg views standardize SQL view definitions across engines, enabling view federation across Polaris, Nessie, and Gravitino catalogs.
How to build a Model Context Protocol (MCP) server that exposes lakehouse tables and semantic views as AI-accessible tools, with Python implementation.
Microsoft Build 2026 revealed an agentic analytics stack built on Fabric IQ, OneLake Iceberg support, and semantic models.
