  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.
