As the Lakehouse architecture becomes more widespread, ensuring the
reproducibility of data workloads over data lakes emerges as a crucial concern
for data engineers. However, achieving reproducibility remains challenging. The
size of data pipelines contributes to slow testing and iterations, while the
intertwining of business logic and data management complicates debugging and
increases error susceptibility. In this paper, we highlight recent advancements
made at Bauplan in addressing this challenge. We introduce a system designed to
decouple compute from data management, by leveraging a cloud runtime alongside
Nessie, an open-source catalog with Git semantics. Demonstrating the system's
capabilities, we showcase its ability to offer time-travel and branching
semantics on top of object storage, and offer full pipeline reproducibility
with a few CLI commands.

介绍了在面临数据湖中数据工作负载可重现性的挑战时，Bauplan 在解决此问题方面的最新进展，通过将计算与数据管理解耦，并利用具有 Git 语义的开源目录 Nessie 以及云运行时，展示了系统提供时间旅行和分支语义以及通过少数 CLI 命令实现完整的管道可重现性的能力。