Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Samiya Khan, Xiufeng Liu, Syed Arshad Ali, Mansaf Alam

TL;DR
This paper analyzes and compares various storage solutions for big data systems, focusing on NoSQL data models, file formats, and emerging decentralized storage technologies, to guide developers in making informed choices.
Contribution
It provides a comprehensive feature analysis of four main NoSQL data models, evaluates 80 NoSQL solutions, and compares big data file formats and future storage trends.
Findings
Document-oriented, key-value, graph, and wide-column models have distinct advantages and use cases.
Among 80 NoSQL solutions, specific features align with different application needs.
Decentralized storage and blockchain present future directions with unique challenges.
Abstract
Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCloud Computing and Resource Management · IoT and Edge/Fog Computing · Big Data and Business Intelligence
