This paper presents the SLEGO (Software-Lego) system, a collaborative analytics platform that bridges the gap between experienced developers and novice users using a cloud-based platform with modular, reusable microservices. These microservices enable developers to share their analytical tools and workflows, while a simple graphical user interface (GUI) allows novice users to build comprehensive analytics pipelines without programming skills. Supported by a knowledge base and a Large Language Model (LLM) powered recommendation system, SLEGO enhances the selection and integration of microservices, increasing the efficiency of analytics pipeline construction. Case studies in finance and machine learning illustrate how SLEGO promotes the sharing and assembly of modular microservices, significantly improving resource reusability and team collaboration. The results highlight SLEGO's role in democratizing data analytics by integrating modular design, knowledge bases, and recommendation systems, fostering a more inclusive and efficient analytical environment.

翻译：本文介绍了SLEGO（软件乐高）系统，这是一个协作分析平台，通过基于云的模块化可复用微服务架构，弥合了经验丰富的开发者与新手用户之间的鸿沟。这些微服务使开发者能够共享其分析工具和工作流，而简单的图形用户界面（GUI）则允许不具备编程技能的新手用户构建完整的分析流水线。在知识库和基于大语言模型（LLM）的推荐系统的支持下，SLEGO增强了微服务的选择与集成，提高了分析流水线构建的效率。金融和机器学习领域的案例研究表明，SLEGO如何促进模块化微服务的共享与组装，显著提升了资源可复用性和团队协作效率。研究结果凸显了SLEGO通过整合模块化设计、知识库和推荐系统，在推动数据分析民主化方面的作用，从而营造了一个更具包容性和高效性的分析环境。