GitLake: Git-for-data for the agentic lakehouse
Preprint of the paper accepted at DASHSys, VLDB 2026, Boston, USA, in collaboration with Columbia University, University of Wisconsin-Madison, Carnegie Mellon University
Jacopo Tagliabue
Jul 10, 2026
Abstract
We present GitLake, a Git-for-data design for an agent-first lakehouse. The system lifts single-table Iceberg snapshots into lakehouse wide commits, branches, and merges, letting agents work on isolated branches while humans review and publish changes. Pipelines run on temporary branches and publish through a final merge, so all outputs become visible atomically or none do. Finally, we report production lessons as well as correctness insights from a preliminary Alloy model of our core abstractions.
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