Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby massive amounts of heterogeneous data are gathered from multiple sources, managed, analyzed (in batch, stream or hybrid fashion), and served to end-users and external applications. Such systems pose specific challenges in all phases of software development lifecycle and might become very complex by evolving data, technologies, and target value over time. Consequently, many organizations and enterprises have found it difficult to adopt BDSs. In this article, we provide insight into three major activities of software engineering in the context of BDSs as well as the choices made to tackle them regarding state-of-the-art research and industry efforts. These activities include the engineering of requirements, designing and constructing software to meet the specified requirements, and software/data quality assurance. We also disclose some open challenges of developing effective BDSs, which need attention from both researchers and practitioners.

中文翻译：

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大数据系统

大数据系统 (BDS) 是一类新兴的可扩展软件技术，通过它可以从多个来源收集、管理、分析（以批处理、流式或混合方式）的大量异构数据，并为最终用户和外部应用程序提供服务。这样的系统在软件开发生命周期的所有阶段都提出了特定的挑战，并且随着时间的推移数据、技术和目标价值的演变可能变得非常复杂。因此，许多组织和企业发现很难采用 BDS。在本文中，我们提供了对 BDS 环境下软件工程的三个主要活动的见解，以及在最先进的研究和行业努力方面为解决这些活动而做出的选择。这些活动包括需求工程，设计和构建满足特定要求的软件，以及软件/数据质量保证。我们还披露了开发有效 BDS 的一些公开挑战，这些挑战需要研究人员和从业者的关注。