#13 · Primary category： AI Data Infrastructure & Storage
lance
Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..
Project last updated：08/29/26
GitHub Stars
7.0K
Forks
826
Contributors
269
License
Apache-2.0
Why we included this project
Unstructured data with vectors, images, and tabular fields usually needs a handful of different stores, and teams end up stitching them together. Lance packs all of it into a single columnar format and adds hybrid search over the same dataset, combining vector similarity, full-text BM25, and plain SQL analytics. It reads through pandas, DuckDB, Polars, and PyArrow, so you don't have to leave the Python tooling you already know. Random access is fast, columns can be added with backfilled values instead of full rewrites, and every change is versioned with ACID transactions, which keeps feature engineering and training data reproducible without much ceremony.
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