2024/04/21 by Jacopo Tagliabue, Tagliabue, Jacopo, Ciro Greco +1 · 3 citations
Computer Science · Decision Sciences · #Big Data Technologies and Applications #Data Visualization and Analytics #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2404.13682
openalex publication_date 2024/04/21 · openalex created_date 2024/04/25 · openalex updated_date 2026/07/28
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.