DuckLake is a next generation data lakehouse and open table format. It is significantly simpler and faster than Apache Iceberg or Delta Lake because it uses a SQL database for storing the catalog and metadata. This post covers an architecture deep dive with a detailed Q&A section. DuckLake consists of 3 components: storage, catalog, and compute. Each can be scaled independently, hosted locally with ease, and deployed to the cloud for production. MotherDuck offers managed DuckLake lakehouses for additional simplicity and performance.

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A detailed Q&A from a DuckLake webinar covering architecture, deployment, and operational questions. Topics include schema evolution (no Parquet rewrites for column changes), multi-tenancy via partitioning, concurrency model (~100 TPS vs ~1 TPS for Iceberg/Delta), catalog options (PostgreSQL, MotherDuck, Quack protocol), upsert support via MERGE, caching behavior (RAM today, disk extensions coming), migration from Iceberg (metadata-only, files stay in place) and Delta (data move required), dbt/SQLMesh compatibility, time-series use cases, and maintenance via checkpoint/vacuum/compact commands. DuckLake is MIT-licensed and open source; MotherDuck is the managed commercial offering.

DuckLake Architecture Deep Dive