Abstract
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 language | English |
|---|---|
| Title of host publication | Proceedings of the 2017 IEEE 2nd International conference on big data analysis (ICBDA 2017) |
| Place of Publication | United States |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 89-93 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509036196 |
| ISBN (Print) | 9781509036189, 9781509036202 |
| DOIs | |
| Publication status | Published - 23 Oct 2017 |
| Event | 2nd IEEE International Conference on Big Data Analysis: ICBDA 2017 - Beijing Post Hotel, Beijing, China Duration: 10 Mar 2017 → 12 Mar 2017 https://web.archive.org/web/20161110104944/http://www.icbda.org/index.html (Conference website) http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8053962 (Conference proceedings) |
Conference
| Conference | 2nd IEEE International Conference on Big Data Analysis |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 10/03/17 → 12/03/17 |
| Other | In 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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