Analysis updated 2026-07-18 · repo last pushed 2025-09-29
Attach a DuckLake database with a SQL command to build a lightweight data lake without cloud warehouse costs.
Query data as it existed at an earlier point in time for audits or to recover accidentally deleted rows.
Add new columns to a table via schema evolution without breaking existing queries.
Track exactly which rows were inserted, updated, or deleted to sync data to other systems.
| carlopi/ducklake2 | achanana/mavsdk | alange/llama.cpp | |
|---|---|---|---|
| Stars | — | — | 0 |
| Language | C++ | C++ | C++ |
| Last pushed | 2025-09-29 | 2024-05-20 | — |
| Maintenance | Quiet | Dormant | — |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | 3/5 | 4/5 | 4/5 |
| Audience | data | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Still early-stage (version 0.1), best for experimentation rather than production use.
DuckLake is an extension for DuckDB (a lightweight SQL database engine) that lets you build and manage a data lake, essentially a centralized storage system for your data. Instead of scattering files across your computer or cloud storage, DuckLake organizes everything in one structured format, storing metadata (information about your data) in a catalog and the actual data in Parquet files (a compressed, efficient file format). The big benefit: you can treat your data lake like a normal database, querying and updating it with standard SQL commands. Here's how it works in practice. You attach a DuckLake database using a simple SQL command, point it to where you want to store files, and then create tables and run queries just like you would in any SQL database. When you insert or update data, DuckLake automatically manages the Parquet files and metadata catalog in the background. You don't need to think about file management, the system handles it for you. What makes DuckLake stand out is a few built-in features that data teams find valuable. Time travel lets you query your data as it existed at an earlier point in time, useful for audits or accidentally-deleted data recovery. Schema evolution means you can add new columns to a table without breaking existing code. Change data feeds track exactly which rows were inserted, updated, or deleted, which is essential if you're syncing data to other systems. These features are typically only available in enterprise data platforms, DuckLake brings them into an open, SQL-based format that anyone can use. This project would appeal to data engineers, analysts, or startup founders building data pipelines who want a simple alternative to expensive cloud data warehouses. It's also useful for anyone who currently manages data across multiple CSV or Parquet files and wants a lightweight, queryable catalog system. Since it's still in early development (version 0.1), it's best suited for experimentation rather than critical production systems, but it's open source and free to use.
A DuckDB extension that turns Parquet files into a queryable SQL data lake, adding time travel, schema evolution, and change tracking on top.
Mainly C++. The stack also includes C++, DuckDB, SQL.
Quiet — no commits in 6-12 months (last push 2025-09-29).
Open source and free to use, per the explanation, though the specific license terms aren't stated.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly data.
This repo across BitVibe Labs
Verify against the repo before relying on details.