# Bauplan: zero-copy, scale-up FaaS for data pipelines

arXiv: https://arxiv.org/abs/2410.17465 · HTML: https://mlsanity.com/paper/2410.17465/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 22 Oct 2024.

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

- [Operationalizing Machine Learning: An Interview Study](https://mlsanity.com/paper/2209.09125/) (2022)
- [Towards Scalable Dataframe Systems](https://mlsanity.com/paper/2001.00888/) (2020)
- [How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature](https://mlsanity.com/paper/1803.10311/) (2018)
- [Building a serverless Data Lakehouse from spare parts](https://mlsanity.com/paper/2308.05368/) (2023)
- [Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie](https://mlsanity.com/paper/2404.13682/) (2024)
- [Formal Foundations of Serverless Computing](https://mlsanity.com/paper/1902.05870/) (2019)
- [An Empirical Study of Developers' Challenges in Implementing Workflows as Code: A Case Study on Apache Airflow](https://mlsanity.com/paper/2406.00180/) (2024)
- [Triggerflow: Trigger-based Orchestration of Serverless Workflows](https://mlsanity.com/paper/2006.08654/) (2020)

## Built on by (most cited first, 2 of 2)

- [Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse](https://mlsanity.com/paper/2510.09567/) (2025)
- [Building a Correct-by-Design Lakehouse. Data Contracts, Versioning, and Transactional Pipelines for Humans and Agents](https://mlsanity.com/paper/2602.02335/) (2026)

## Abstract (arXiv)

Chaining functions for longer workloads is a key use case for FaaS platforms in data applications. However, modern data pipelines differ significantly from typical serverless use cases (e.g., webhooks and microservices); this makes it difficult to retrofit existing pipeline frameworks due to structural constraints. In this paper, we describe these limitations in detail and introduce bauplan, a novel FaaS programming model and serverless runtime designed for data practitioners. bauplan enables users to declaratively define functional Directed Acyclic Graphs (DAGs) along with their runtime environments, which are then efficiently executed on cloud-based workers. We show that bauplan achieves both better performance and a superior developer experience for data workloads by making the trade-off of reducing generality in favor of data-awareness