Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie

TL;DR

This paper presents a system that enhances reproducibility of data pipelines over data lakes by decoupling compute from data management, enabling time-travel, branching, and easy pipeline reproduction using Bauplan and Nessie.

Contribution

The paper introduces a novel system combining Bauplan and Nessie to achieve reproducible, replayable data pipelines with minimal commands, addressing key challenges in Lakehouse architectures.

Findings

Supports time-travel and branching semantics on object storage

Enables full pipeline reproducibility with simple CLI commands

Decouples compute from data management for faster testing

Abstract

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.