GitLake lifts Iceberg snapshots into lakehouse-wide commits, branches, and merges so agents develop in isolation and multi-table pipelines publish atomically via temporary-branch merges.
Building a serverless Data Lakehouse from spare parts
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abstract
The recently proposed Data Lakehouse architecture is built on open file formats, performance, and first-class support for data transformation, BI and data science: while the vision stresses the importance of lowering the barrier for data work, existing implementations often struggle to live up to user expectations. At Bauplan, we decided to build a new serverless platform to fulfill the Lakehouse vision. Since building from scratch is a challenge unfit for a startup, we started by re-using (sometimes unconventionally) existing projects, and then investing in improving the areas that would give us the highest marginal gains for the developer experience. In this work, we review user experience, high-level architecture and tooling decisions, and conclude by sharing plans for future development.
fields
cs.DB 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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GitLake: Git-for-data for the agentic lakehouse
GitLake lifts Iceberg snapshots into lakehouse-wide commits, branches, and merges so agents develop in isolation and multi-table pipelines publish atomically via temporary-branch merges.