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。

基于 Bauplan 和 Nessie 的可重现数据湖数据科学：可回放数据流水线

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

We describe a system for building task-oriented dialogue systems combining
the in-context learning abilities of large language models (LLMs) with the
deterministic execution of business logic. LLMs are used to translate between
the surface form of the conversation and a domain-specific language (DSL) which
is used to progress the business logic. We compare our approach to the
intent-based NLU approach predominantly used in industry today. Our experiments
show that developing chatbots with our system requires significantly less
effort than established approaches, that these chatbots can successfully
navigate complex dialogues which are extremely challenging for NLU-based
systems, and that our system has desirable properties for scaling task-oriented
dialogue systems to a large number of tasks. We make our implementation
available for use and further study.

使用大型语言模型（LLMs）的上下文学习能力与业务逻辑的确定执行相结合，描述了一种构建面向任务的对话系统的系统。与主要用于行业的基于意图的 NLU 方法相比较，我们的实验表明，使用我们的系统开发聊天机器人所需的工作量明显较少，这些聊天机器人可以成功地进行复杂对话，并且我们的系统对于大规模任务导向的对话系统具有可扩展的有益特性。我们提供了我们的实现用于使用和进一步研究。