Big-data NoSQL databases: A comparison and analysis of 'Big-Table', 'DynamoDB', and 'Cassandra'

Sultana Kalid, Ali Syed, Azeem Mohammad, Malka N. Halgamuge

Research output: Book chapter/Published conference paperConference paperpeer-review

27 Citations (Scopus)


The growth and enhancement of technology in the corporate society has led to data storage and confidentiality issues. The problem arises from the management of trillions of data, generated every second in corporations, precisely known as 'Big Data'. Big Data needs to be stored and managed by larger companies that do not have the right storage systems, as there is not any developed yet. The aim of this paper is to find a solution to this growing problem by analyzing gaps in the literature, and to evaluate possible solutions. This study has analyzed content from top reviewed scientific publications, to gather compared and contrasted data from articles and highlight gaps. The highlighted literature will address this problems, and find solutions by contrasting BigData management approaches of NoSQL databases; BigTable, DynamoDB, and Cassandra. The findings summarized from publications are highlighted and the main features of all three databases and their applications are displayed. The system performances are analyzed based on their consistency, availability and partition intolerance. The study concluded that Google's BigTable and Amazon's DynamoDB are also critical and efficient on their own, and also found that the combination of both systems had caused the development of Cassandra. Cassandra is now the primary focus of numerous companies to develop different applications. Furthermore, all three systems are NoSQL storage systems, and BigTable, and based on one master node approach, unlike Dynamo, and Cassandra, it follows a Peer-to-Peer system. BigTable however, with some additional features from DynamoDB has helped the development of Cassandra, which is the basis of various modern applications available both open source and socially.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE 2nd International conference on big data analysis (ICBDA 2017)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781509036196
ISBN (Print)9781509036189, 9781509036202
Publication statusPublished - 23 Oct 2017
Event2nd IEEE International Conference on Big Data Analysis: ICBDA 2017 - Beijing Post Hotel, Beijing, China
Duration: 10 Mar 201712 Mar 2017 (Conference website) (Conference proceedings)


Conference2nd IEEE International Conference on Big Data Analysis
OtherIn recent years, "Big Data" has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. The 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA 2017) provides a leading forum for disseminating the latest research in Big Data Research, Development, and Application. ICBDA1017 is co-organized by IEEE and Research Institute of Big Data Analytics, Xi'an Jiaotong-Liverpool University, China. Assisted by University of Texas at Dallas, USA. Prof. Steven Guan (Xi'an Jiaotong-Liverpool University, China) and Prof. Kang Zhang (University of Texas at Dallas, USA) take charge of the Conference Co-chair.
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