Vol. 1 No. 1 (2021): African Journal of Artificial Intelligence and Sustainable Development
Articles

Leveraging Cloud Object Storage Mechanisms for Analyzing Massive Datasets

PDF
  • Sarbaree Mishra
Sarbaree Mishra
Program Manager at Molina Healthcare Inc., USA
Cover

Published 12-01-2021

Keywords

  • Cloud object storage,
  • big data analytics,
  • scalability
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Sarbaree Mishra, “Leveraging Cloud Object Storage Mechanisms for Analyzing Massive Datasets”, African J. of Artificial Int. and Sust. Dev., vol. 1, no. 1, pp. 286–306, Jan. 2021, Accessed: Dec. 17, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/217

Abstract

Cloud object storage has become a cornerstone for managing and analyzing large datasets, offering an efficient and flexible solution for organizations to store and process unstructured and structured data. With the exponential growth of data across various industries, traditional data storage solutions often need help to keep up with the sheer volume and variety of information. With its scalability, durability, and cost-effectiveness, cloud object storage addresses these challenges by providing a centralized platform where businesses can store vast amounts of data and access it seamlessly. This article explores how cloud object storage when paired with modern data analytics tools, enables organizations to unlock valuable insights from massive datasets. The architecture behind cloud object storage is designed to handle high-volume data efficiently while offering flexible access and robust security features. In big data analytics, cloud object storage plays a pivotal role by facilitating data processing at scale & supporting advanced analytics techniques like machine learning and artificial intelligence. Using data lakes and distributed computing frameworks, cloud object storage ensures that large datasets are accessible for real-time analysis, driving faster & more informed decision-making. The article also delves into performance optimization strategies for cloud object storage, such as data tiering and caching, which improve access speed and reduce costs. It also highlights several use cases from industries like finance, healthcare, and e-commerce, demonstrating how organizations leverage cloud object storage to gain competitive advantages. By capitalizing on cloud storage's flexibility, businesses can overcome traditional storage limitations & explore new avenues for innovation and growth. In summary, cloud object storage simplifies the management of massive datasets & enables organizations to harness the power of data-driven decision-making in an ever-evolving digital landscape.

PDF

Downloads

Download data is not yet available.

References

  1. Rupprecht, L., Zhang, R., Owen, B., Pietzuch, P., & Hildebrand, D. (2017, April). SwiftAnalytics: Optimizing object storage for big data analytics. In 2017 IEEE International Conference on Cloud Engineering (IC2E) (pp. 245-251). IEEE.
  2. Chen, H. M., Chang, K. C., & Lin, T. H. (2016). A cloud-based system framework for performing online viewing, storage, and analysis on big data of massive BIMs. Automation in Construction, 71, 34-48.
  3. Dey, S., Chakraborty, A., Naskar, S., & Misra, P. (2012, October). Smart city surveillance: Leveraging benefits of cloud data stores. In 37th Annual IEEE Conference on Local Computer Networks-Workshops (pp. 868-876). IEEE.
  4. Armbrust, M., Das, T., Sun, L., Yavuz, B., Zhu, S., Murthy, M., ... & Zaharia, M. (2020). Delta lake: high-performance ACID table storage over cloud object stores. Proceedings of the VLDB Endowment, 13(12), 3411-3424.
  5. Belcastro, L., Marozzo, F., Talia, D., & Trunfio, P. (2017). Big data analysis on clouds. Handbook of big data technologies, 101-142.
  6. Adedugbe, O., Benkhelifa, E., Campion, R., Al-Obeidat, F., Bani Hani, A., & Jayawickrama, U. (2020). Leveraging cloud computing for the semantic web: review and trends. Soft Computing, 24(8), 5999-6014.
  7. Qolomany, B., Al-Fuqaha, A., Gupta, A., Benhaddou, D., Alwajidi, S., Qadir, J., & Fong, A. C. (2019). Leveraging machine learning and big data for smart buildings: A comprehensive survey. IEEE access, 7, 90316-90356.
  8. Cai, H., Xu, B., Jiang, L., & Vasilakos, A. V. (2016). IoT-based big data storage systems in cloud computing: perspectives and challenges. IEEE Internet of Things Journal, 4(1), 75-87.
  9. Chen, J., Douglas, C., Mutsuzaki, M., Quaid, P., Ramakrishnan, R., Rao, S., & Sears, R. (2012, May). Walnut: a unified cloud object store. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (pp. 743-754).
  10. Fazio, M., Celesti, A., Puliafito, A., & Villari, M. (2015). Big data storage in the cloud for smart environment monitoring. Procedia Computer Science, 52, 500-506.
  11. Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412-421.
  12. Brim, M. J., Dillow, D. A., Oral, S., Settlemyer, B. W., & Wang, F. (2013, November). Asynchronous object storage with QoS for scientific and commercial big data. In Proceedings of the 8th parallel data storage workshop (pp. 7-13).
  13. Yaseen, M. U., Anjum, A., Rana, O., & Hill, R. (2018). Cloud-based scalable object detection and classification in video streams. Future Generation Computer Systems, 80, 286-298.
  14. Atitallah, S. B., Driss, M., Boulila, W., & Ghézala, H. B. (2020). Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review, 38, 100303.
  15. Ahmed, E. S. A., & Saeed, R. A. (2014). A survey of big data cloud computing security. International Journal of Computer Science and Software Engineering (IJCSSE), 3(1), 78-85.
  16. Thumburu, S. K. R. (2020). Large Scale Migrations: Lessons Learned from EDI Projects. Journal of Innovative Technologies, 3(1).
  17. Thumburu, S. K. R. (2020). Leveraging APIs in EDI Migration Projects. MZ Computing Journal, 1(1).
  18. Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).
  19. Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
  20. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
  21. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
  22. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
  23. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
  24. Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
  25. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).

Similar Articles

You may also start an advanced similarity search for this article.