Summary

Users are able to run multiple DuckLake clients simultaneously to connect concurrently to PostgreSQL, MySQL, or SQLite, allowing them to utilize the same DuckLake dataset. This feature ensures that different data flows from multiple entry points can interact effectively. Additionally, if you choose to use DuckDB for both your application entry point and the catalog database, you can still benefit from DuckLake. You can benefit from time travel queries, exploit data partitioning, and store your data across multiple files instead of relying on a single potentially large database file. This architecture significantly enhances flexibility for data-heavy applications that need robust parallel processing.

Title

DuckLake is an integrated data lake and catalog format – DuckLake

Description

DuckLake delivers advanced data lake features without traditional lakehouse complexity by using Parquet files and your SQL database. It's an open, standalone format from the DuckDB team.

Keywords

catalog, database, data, format, storage, extension, lake, parquet, files, documentation, specification, features, using, client, multiple, time, travel

NS Lookup

A 104.21.69.163, A 172.67.210.92

Dates

Created 2026-02-15

Updated 2026-02-15

Summarized 2026-03-22