lakeFS

The page covers data engineering trends, machine learning tutorials, and practices for managing data with lakeFS.

The page explains how Airflow and lakeFS can be used to build reproducible data pipelines with commits, without modifying job code or logic.

The page compares data warehouses and data lakes and covers practices for choosing between them.

The company describes how Go concurrency helped overcome a Postgres performance barrier.

The page features lakeFS news, press coverage, and company updates, and discusses how Git for Data is shaping the data infrastructure space.

The page lists open roles and career opportunities with the lakeFS team, which works on data version control.

The Director of Product Management drives product strategy and execution at lakeFS, leads core product direction, and works closely with R&D, GTM, and CS.

The page compares lakeFS plans, pricing, and features and describes scaling data versioning across infrastructure.

The page compares Amazon S3 Mountpoint and lakeFS Mount.

The page introduces the company behind the lakeFS open-source project and discusses why lakeFS was founded and the inspiration for its logo.

The page provides a way to contact the team about questions, contributions, or technical support.

Bridging the data Infrastructure gap for AI. Ensure AI data governance, make AI training and agent runs reproducible, reduce access friction.

lakeFS states that it protects personal data and aims for transparency in how it handles that data.