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A Review of Polyglot Persistence in the Big Data World

Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 10083, China
Author to whom correspondence should be addressed.
Information 2019, 10(4), 141;
Received: 10 March 2019 / Revised: 31 March 2019 / Accepted: 4 April 2019 / Published: 16 April 2019
(This article belongs to the Section Review)
The inevitability of the relationship between big data and distributed systems is indicated by the fact that data characteristics cannot be easily handled by a standalone centric approach. Among the different concepts of distributed systems, the CAP theorem (Consistency, Availability, and Partition Tolerant) points out the prominent use of the eventual consistency property in distributed systems. This has prompted the need for other, different types of databases beyond SQL (Structured Query Language) that have properties of scalability and availability. NoSQL (Not-Only SQL) databases, mostly with the BASE (Basically Available, Soft State, and Eventual consistency), are gaining ground in the big data era, while SQL databases are left trying to keep up with this paradigm shift. However, none of these databases are perfect, as there is no model that fits all requirements of data-intensive systems. Polyglot persistence, i.e., using different databases as appropriate for the different components within a single system, is becoming prevalent in data-intensive big data systems, as they are distributed and parallel by nature. This paper reflects the characteristics of these databases from a conceptual point of view and describes a potential solution for a distributed system—the adoption of polyglot persistence in data-intensive systems in the big data era. View Full-Text
Keywords: polyglot persistence; big data; ACID; BASE; CAP theorem; SQL; NoSQL polyglot persistence; big data; ACID; BASE; CAP theorem; SQL; NoSQL
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Khine, P.P.; Wang, Z. A Review of Polyglot Persistence in the Big Data World. Information 2019, 10, 141.

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