#lakehouse

Kafka was never built to be queried. Apache Fluss bolts a columnar, Arrow-native hot store onto the streaming layer and tiers cold to Iceberg — a clean full-stack realtime design whose only real open question is governance.

"Catalog" means two different things in the lakehouse: the technical catalog in your query path (Unity, Polaris) and the governance catalog beside it (OpenMetadata, DataHub). The second is where lineage, ownership, and trust live — and where the next fight is.

Iceberg and Delta reimplemented a transactional catalog as JSON and Avro files in object storage — and then needed a real database catalog on top anyway. DuckLake's heresy is to skip the file layer entirely: put all the metadata in SQL, keep the data in Parquet. It is both obvious and a little rude.

Iceberg did not win on features. Delta Lake had the bigger installed base and Hudi had the better write path. Iceberg won on governance and an engine-neutral spec, and the moment Databricks paid roughly $2B for Tabular the war was effectively over.

A lakehouse on object storage looks cheap because storage is cheap. The bill is built from request count and managed-tier access fees, both of which scale with file count, not data volume. 5 GB stored as one million 5 MB files is a different invoice than 5 GB stored as ten 512 MB files.