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Building a serverless Data Lakehouse from spare parts

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arxiv 2308.05368 v1 pith:K5SVRGKO submitted 2023-08-10 cs.DB cs.DCcs.SE

Jacopo Tagliabue , Ciro Greco , Luca Bigon This is my paper · ORCID
classification cs.DBcs.DCcs.SE
keywords datalakehousearchitecturebuildingexistingexperienceserverlessuser
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Not Your Usual Type(s): Data contracts as types across languages and engines

    cs.DB 2026-07 conditional novelty 6.0 of 10

    Treating data contracts as type annotations enforced at three pipeline stages lets multi-language lakehouse DAGs fail fast on schema mismatches.

  2. GitLake: Git-for-data for the agentic lakehouse

    cs.DB 2026-07 conditional novelty 5.0 of 10

    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.

  3. Eudoxia: a FaaS scheduling simulator for the composable lakehouse

    cs.DB 2025-05 conditional novelty 5.0 of 10

    Eudoxia is a deterministic, open-source simulator for evaluating FaaS scheduling algorithms in composable lakehouses, with a TPC-H-based runtime validation on the Bauplan cloud platform.

  4. FaaS and Furious: abstractions and differential caching for efficient data pre-processing

    cs.DB 2024-11 conditional novelty 5.0 of 10

    A columnar differential cache for lakehouse pipelines reuses overlapping scan fragments and reduces S3 bytes read by up to 30% in preliminary benchmarks.

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