# Building a Correct-by-Design Lakehouse. Data Contracts, Versioning, and Transactional Pipelines for Humans and Agents

arXiv: https://arxiv.org/abs/2602.02335 · HTML: https://mlsanity.com/paper/2602.02335/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025](https://mlsanity.com/paper/2512.14012/) (2025)
- [Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse](https://mlsanity.com/paper/2510.09567/) (2025)
- [Data Pipeline Quality: Influencing Factors, Root Causes of Data-related Issues, and Processing Problem Areas for Developers](https://mlsanity.com/paper/2309.07067/) (2023)
- [Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie](https://mlsanity.com/paper/2404.13682/) (2024)
- [Trustworthy AI in the Agentic Lakehouse: from Concurrency to Governance](https://mlsanity.com/paper/2511.16402/) (2025)
- [Eudoxia: a FaaS scheduling simulator for the composable lakehouse](https://mlsanity.com/paper/2505.13750/) (2025)
- [The Data Lakehouse: Data Warehousing and More](https://mlsanity.com/paper/2310.08697/) (2023)
- [Bauplan: zero-copy, scale-up FaaS for data pipelines](https://mlsanity.com/paper/2410.17465/) (2024)

## Built on by (most cited first, 4 of 4)

- ["Skill Issues'': Data-Centric Optimization of Lakehouse Agents](https://mlsanity.com/paper/2606.01185/) (2026)
- [Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse](https://mlsanity.com/paper/2603.13380/) (2026)
- [Beyond the Data Mesh Illusion: Designing Modern AI-augmented Lakehouses to Bridge the Gap Between Theory and Practice](https://mlsanity.com/paper/2605.27131/) (2026)
- [GitLake: Git-for-data for the agentic lakehouse](https://mlsanity.com/paper/2607.08319/) (2026)

## Abstract (arXiv)

Lakehouses are now the default substrate for analytics and AI, but they remain fragile under concurrent, untrusted change: schema mismatches often surface only at runtime, development and production easily diverge, and multi-table pipelines can expose partial results after failure. We present Bauplan, a code-first lakehouse that aims to eliminate a broad class of these failures by construction. Bauplan builds on a storage substrate that already provides atomic single-table snapshot evolution, and adds three pipeline-level correctness mechanisms: typed table contracts to make transformation boundaries checkable, Git-like data versioning to support reproducible collaboration and review, and transactional runs that guarantee atomic publication of an entire pipeline execution. We describe the system design, show how these abstractions fit together into a unified programming model for humans and agents, and report early results from a lightweight Alloy model that both validates key intuitions and exposes subtle counterexamples around transactional branch visibility. Our experience suggests that correctness in the lakehouse is best addressed not by patching failures after the fact, but by restricting the programming model so that many illegal states become unrepresentable.