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Cited by 88Big Data Technologies and ApplicationsSoftware Engineering Techniques and PracticesCloud Computing and Resource Management

Big Data Systems: A Software Engineering Perspective

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Davoudian, Ali, Liu, Mengchi·2021·ACM Computing Surveys
DOI 10.1145/3408314
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Abstract

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.

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  • Multi-model databases such as PostgreSQL with JSONB or ArangoDB have been designed to feature the best of both worlds with trade-offs between consistency, scalability, and developer ease.

    “The trend has now culminated in the hybrid stage, where multi-model databases such as PostgreSQL with JSONB or ArangoDB have been designed to feature the best of both worlds with trade-offs between consistency, scalability, and developer ease (Davoudian & Liu, 2020).”

    From A REVIEW ON OBJECT-ORIENTED, OBJECT-RELATIONAL, RELATIONAL, AND NOSQL DATABASES: EVOLUTION, INTEGRATION, AND PERFORMANCE TRADE-OFFS (2025) · cites this paper as Davoudian & Liu, 2020

    Section 3 (BDS Architecture Design; page unavailable) discusses hybrid data storage and trade-offs involving consistency and availability. Section 3.2.6 describes querying across multiple data models, but the cited passages do not identify PostgreSQL with JSONB or ArangoDB as multi-model databases, or discuss a trade-off involving developer ease. (LLM, conf=0.84)

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Authors
Davoudian, A., & Liu, M.
Affiliations
  • Carleton University (CA)
  • South China Normal University (CN)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2020-09-28
Volume
53
Issue
5
Pages
1-39
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3408314
OpenAlex
W3090295042
Cited by
88
References
111
Citations by year
2026: 10 · 2025: 21 · 2024: 18 · 2023: 16 · 2022: 13 · 2021: 10
FWCI
9.68
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM Computing Surveys (publishedVersion)
Funders
  • Guangzhou Key Laboratory of Big Data and Intelligent Education (201905010009)
  • National Natural Science Foundation of China (61672389)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
Keywords
big data systems, requirements engineering, software design, software construction, data quality assurance, data heterogeneity, scalability, software engineering challenges

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