Every production Iceberg data lake eventually hits the same wall: tables that looked fast at 10 GB start crawling at 10 TB. Small files pile up from streaming ingestion, snapshots accumulate because nobody set expiration, orphaned data lingers from failed Spark jobs, and manifest lists grow until planning a simple SELECT takes longer than running it. Netflix hit this wall years ago — and their solution shaped how the industry thinks about lakehouse architecture. At AWS re:Invent, their engineers walked through the ecosystem they assembled around Iceberg: Polaris for catalog management, Autotune for automated compaction, janitors for continuous cleanup, and Metacat for observability. The outcome was a 25% cost reduction and tables that stayed healthy without manual intervention. But Netflix had something most teams don't: a dedicated platform organization building custom distributed services backed by CockroachDB, Kafka, and fleets of Spark clusters.…