lakeFS

license

Apache-2.0(OSI)

source

public

core-gated

ungated

Apache-2.0 license body confirmed. The repository is public and unarchived and builds the whole product, and the README describes no paid tier, enterprise edition or license-gated build beside it, so source is public and the core ungated.

• https://github.com/treeverse/lakeFS/blob/master/LICENSE recorded 2026-08-18

  LICENSE file is the verbatim Apache-2.0 text

• https://api.github.com/repos/treeverse/lakeFS recorded 2026-08-18

  Repo metadata - license spdx_id Apache-2.0, private false, archived false, default branch master - for treeverse/lakeFS.

• https://github.com/treeverse/lakeFS/blob/master/README.md recorded 2026-08-18

  README describes transformation of object storage into a Git-like repository giving repeatable, atomic and versioned data lake operations over AWS S3, Azure Blob Storage and Google Cloud Storage, S3-API compatible and working with Spark, Hive and other data frameworks, with no paid tier, enterprise edition or license-key-gated build beside it.

5,490 GitHub stars, which lands in the 1K-10K stars band of the stars scale, level 2. The `lakefs` package on PyPI is the Python SDK wrapper for the server's API rather than the server, so its downloads measure client use. No deployment figure is published, leaving the stars scale and its cap of 3.

• https://api.github.com/repos/treeverse/lakeFS recorded 2026-08-18

  Repo metadata - stargazers_count = 5,490 - for treeverse/lakeFS.

Banded on the category feature matrix as single-purpose store or index. Placed two bands below the vespa anchor, on a matrix that bands on how much of the persist-index-retrieve path a product provides itself, at what scale and over how many retrieval modes.

• https://github.com/treeverse/lakeFS/blob/master/README.md recorded 2026-08-18

  README still documents transformation of object storage into a Git-like repository giving repeatable, atomic and versioned data lake operations over AWS S3, Azure Blob Storage and Google Cloud Storage, S3-API compatible and working with Spark, Hive and other data frameworks.