The Future of Data Storage: A Case Study with the Saudi Company
Journal of Electrical and Electronic Engineering
Volume 6, Issue 1, February 2018, Pages: 1-11
Received: Sep. 28, 2017; Accepted: Oct. 14, 2017; Published: Jan. 11, 2018
Views 2354      Downloads 226
Azzah Al Ghamdi, College of Computer Science and Information Technology, Computer Information Systems Department, Imam Abdalrhman Bin Faisal University, Dammam, Kingdom of Saudi Arabia
Thomas Thomson, School of Computer Science, Information Technology Department, University of Manchester, Manchester, United Kingdom
Article Tools
Follow on us
The age of big data has emerged. These data are generated from online transactions, emails, posts, videos, search queries, etc. People also produce data by using the Internet of Things (IoT) applications and devices. Storing these massive quantities of data has become one of the most important and critical issues for big companies like Google, LinkedIn, Yahoo, and for the digital society in general. Traditional data storage methods such as Relational Database Management Systems (RDBMSs) are coming under increase pressure due to their capability limitations. However, many of new technical solutions have proved their efficiency in storing big data for large companies; some examples of these solutions include NetApp, Hadoop, SAN, the cloud, data centres, etc. The storage, accessibility, and security of big data issues are not only a computer science concern; it has become a topic of interest in many fields such as healthcare, E-commerce, and business in general. This project is investigating the data storage methods and future requirements for one of the largest oil companies in the world, Saudi SA Oil Company. A survey was carried out to understand current storage problems, and collect requirements to implement effective storage methodologies that overcome most of SA’s storage difficulties.
Big Data, SA Company, NetApp Storage, Two Surveys, Analysing Tools
To cite this article
Azzah Al Ghamdi, Thomas Thomson, The Future of Data Storage: A Case Study with the Saudi Company, Journal of Electrical and Electronic Engineering. Vol. 6, No. 1, 2018, pp. 1-11. doi: 10.11648/j.jeee.20180601.11
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Barlow, M., (2013) Real-Time Big Data Analytics: Emerging Architecture. 1st edn. Sebastopol: O’Reilly Media. [Online] Available at: (Accessed: 30 January 2015).
Battles, B., Belleville, C., Grabau, S., and Maurier, J. (2007) REDUCING DATA CENTER POWER CONSUMPTION THROUGH EFFICIENT STORAGE. [Online] Available at: (Accessed: 22 April 2015).
Bronk, C., and Tikk-Ringas, E. (2013) The Cyber Attack on Saudi Aramco. 55(2) pp. 81-96. [Online] Available at: (Accessed: 11 June 2015).
Chen, M., Mao, S., Zhang, Y., and Leung, V. C. (2014) ‘Big Data Related Technologies, Challenges and Future Prospects’. 1st edn. United Kingdom: Cham: Springer.
Clarke, R. (2009) Overview of Storage and Data Management Industry Trends in Long Term Information Retention and Preservation. [Online] Available at: (Accessed: 11 June 2015).
Davenport, T. and Dyche’, J. (2013) Big Data in Big Companies. [Online] Available at: (Accessed: 26 January 2015).
David, L., and Ledoux, T. (2009) Optimizing Data Storage and Management for Petrel Seismic Interpretation and Reservoir Modeling A White Paper for Upstream Oil and Gas Data Managers. Schlumberger. [Online] Available at: (Accessed: 2 April 2015).
Gartner. (2015) [Online] Available at: (Accessed: 31 March 2015).
Godbole, R., and Davied, M. (2006) CATIA V5 Deployments with NetApp Solutions, pp. 1-26. [Online] Available at: (Accessed: 19 June 2015).
Greenberg, A., Hamilton, J., Jain, N., Kandula, S., Kim, C., Lahiri, P., Maltz, D., Patel, P., Sengupta, S. (2009) VL2: A Scalable and Flexible Data Center Network, Microsoft Research, pp. 51-62. [Online] Available at: (Accessed: 6 April 2015).
Intel IT Centre. (2012) Peer Research Big Data Analytics Intel’s IT Manager Survey on How Organizations Are Using Big Data. [Online] Available at: (Accessed: 26 March 2015).
Lillibridge, M., Eshghi, K., and Bhagwat, D. (2013)¬ Improving Restore Speed for Backup Systems that Use Inline Chunk-Based Deduplication, USENIX Association 11th USENIX Conference on File and Storage Technologies (FAST ’13) HP Labs, HP Storage. pp. 183-197. [Online] Available at: (Accessed: 5 June 2015).
Mohammad, A., Mcheick, H., and Grant, E. (2014) Big Data Architecture Evolution: 2014 and Beyond [Online]. Available at: (Accessed: 27 February 2015).
Moniruzzaman, A., and Hossain, S. (2013) NoSQL Database: New Era of Databases for Big data Analytics –Classification, Characteristics and Comparison, International Journal of Database Theory and Application. pp. 1-14, 6(4). [Online] Available at: (Accessed: 30 April 2015).
NetApp. (2009) Application Solution NetApp Data Management for Decision Support Systems [Online] Available at: (Accessed: 22 May 2015).
NetApp. (2011) NetApp Storage Encryption (NSE) Full disk encryption that protects data at rest with no operational impact. [Online] Available at: (Accessed: 29 May 2015).
NetApp. (2014) Datasheet Clustered Data ONTAP Operating System Respond more quickly to business changes and new opportunities—on premises or in the cloud. [Online] Available at: (Accessed: 5 June 2015).
Poulton, N. (2014) Data Storage Networking Real-World skills for the CompTIA Storage+™ Certification and Beyond. 1st edn. Canada: John Wiley & Sons, Inc., Indianapolis, Indiana.
SAGIROGLU, S., and SINANC, D. (2013) Big Data: A Review. pp. 42-47 [Online] Available at: (Accessed: 19 March 2015).
Saudi Aramco. (2015a) 1930s. [Online] Available at: (Accessed: 26 January 2015).
Saudi Aramco. (2015b) Our business. [Online] Available at: (Accessed: 3 April 2015).
SEOBOOK. (2003) KEYWORD-DENSITY ANALYZER. [Online] Available at: (Accessed: 27 May 2015).
Slack, E. (2012) Storage Infrastructures for Big Data Workflows, Storage Switzerland White Paper. [Online] Available at: (Accessed: 29 April 2015).
Smith, D. (2012) Real-Time Predictive Analytics with Big Data from deployment to production, Revolution Analytics. [Online] Available at: (Accessed: 15 April 2015).
Villars, R., and Olofson, C. (2011) Big Data: What It Is and Why You Should Care, IDC Analyzed the future. [Online] Available at: (Accessed: 3 March 2015).
Yuan, D., Yang, Y., Liu, X., and Chen, J. (2010) A Cost-Effective Strategy for Intermediate Data Storage in Scientific Cloud Workflow Systems, pp. 1-12. [Online] Available at: (Accessed: 8 April 2015).
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186