DuckLake is an open lakehouse table format from the DuckDB team. It keeps table metadata in a SQL database and the data in Parquet files. DuckLake 1.0 shipped in April 2026 with a stable specification and guaranteed backward compatibility.
DuckLake is an open lakehouse table format from the DuckDB team. It keeps table metadata in a SQL database and the data in Parquet files. DuckLake 1.0 shipped in April 2026 with a stable specification and guaranteed backward compatibility. This article builds a working DuckLake lakehouse on your laptop with Python and the duckdb package: you will load Parquet data, change rows and schema, time travel, and run maintenance.
A DuckLake has two parts. The data lives in Parquet files in a folder or object store. Everything else, meaning the table list, schemas, snapshots, and file statistics, lives in a catalog database. DuckLake can use any SQL database as its catalog, as long as it supports ACID transactions and primary key constraints. Formats like Apache Iceberg and Delta Lake track much of this in metadata files. In DuckLake, finding the right files is one SQL query.
The docs suggest a catalog by use case: DuckDB for a single client, SQLite for several local clients, and PostgreSQL for a multi-user lakehouse. We use DuckDB, the simplest option. Like other lakehouse formats, DuckLake does not support indexes, primary keys, foreign keys, or UNIQUE and CHECK constraints.
Source: KDnuggets — Read the full article on the original site ↗