Articles Open Access
Scalable Cloud-Enabled Hadoop-Based Distributed Frameworks for Enterprise Business Data Analytics
Cognizant Technologies Solutions, USA
Abstract
The rapid growth of enterprise data generated through digital platforms, social media, cloud applications, and Internet of Things (IoT) devices has created significant challenges for organizations in storing, processing, and analyzing massive datasets. Traditional database systems are often incapable of handling the volume, velocity, and variety of big data efficiently. Hadoop-based distributed frameworks integrated with cloud computing technologies have emerged as scalable and cost-effective solutions for enterprise business analytics. This study explores scalable cloud-enabled Hadoop-based distributed frameworks and their role in enhancing enterprise data analytics capabilities. The framework utilizes distributed storage through Hadoop Distributed File System (HDFS), parallel processing with MapReduce, and cloud infrastructure for elastic resource allocation. The integration of cloud services with Hadoop improves scalability, fault tolerance, processing speed, and accessibility while reducing infrastructure costs. The research further examines the implementation methodologies, architectural components, and practical applications of Hadoop ecosystems in business intelligence and decision-making processes. Additionally, the study evaluates the benefits and limitations of cloud-enabled Hadoop frameworks in enterprise environments. Findings indicate that organizations adopting these frameworks achieve improved operational efficiency, real-time analytics, and competitive advantages. However, challenges related to security, data privacy, and system complexity continue to require advanced research and technological improvements for sustainable enterprise analytics solutions.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 1 |
| Number | 1 |
| Pages | 1-9 |
| Published | 2017-07-05 |
| Issue | Vol. 1 No. 1 (2017): International Research Journal of Innovative Engineering (IRJIE) |
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2. Borthakur, D. (2011). The Hadoop distributed file system: Architecture and design. Hadoop Project Website.
3. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2010). Cloud computing and emerging IT platforms. Future Generation Computer Systems, 25(6), 599–616.
4. Dean, J., & Ghemawat, S. (2010). MapReduce: A flexible data processing tool. Communications of the ACM, 53(1), 72–77.
5. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73.
6. Dittrich, J., & Quiané-Ruiz, J. A. (2012). Efficient big data processing in Hadoop MapReduce. Proceedings of the VLDB Endowment, 5(12), 2014–2015.
7. Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows. IDC.
8. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of big data on cloud computing. Information Systems, 47, 98–115.
9. Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). Big data: Issues and challenges moving forward. Proceedings of the Annual Hawaii International Conference on System Sciences, 995–1004.
10. Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561–2573.
11. Lin, J., & Dyer, C. (2010). Data-intensive text processing with MapReduce. Morgan & Claypool Publishers.
12. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation. McKinsey Global Institute.
13. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology.
14. Omar, Y. M., & Nasr, M. (2015). Cloud computing applications in business analytics. International Journal of Computer Applications, 117(20), 1–7.
15. Rabl, T., Sadoghi, M., Jacobsen, H. A., Gómez-Villamor, S., Mulk, W., & Poess, M. (2012). Solving big data challenges for enterprise application performance management. Proceedings of the VLDB Endowment, 5(12), 1724–1735.
16. Shvachko, K., Kuang, H., Radia, S., & Chansler, R. (2010). The Hadoop distributed file system. Proceedings of IEEE MSST, 1–10.
17. Singh, S., & Reddy, C. K. (2015). A survey on platforms for big data analytics. Journal of Big Data, 2(1), 8–20.
18. White, T. (2012). Hadoop: The definitive guide (3rd ed.). O’Reilly Media.
19. Yadav, S., & Saini, A. (2014). Scalable cloud computing techniques for big data analytics. International Journal of Advanced Research in Computer Science and Software Engineering, 4(5), 112–118.
20. Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. Proceedings of the USENIX Conference on Hot Topics in Cloud Computing, 10–10.
21. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
22. Mathew, A., & Asari, V. K. (2012, August). Local histogram based descriptor for tracking in wide area imagery. In International Conference on Information Processing (pp. 119-128). Berlin, Heidelberg: Springer Berlin Heidelberg.
23. Murugeshwari, B., Sarukesi, K., & Jayakumar, C. (2010, March). An efficient method for knowledge hiding through database extension. In 2010 International Conference on Recent Trends in Information, Telecommunication and Computing (pp. 342-344). IEEE.
24. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
25. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
26. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
27. Kumar, J. (2013). Preservation of the Privacy for Multiple Custodian Systems with Rule Sharing. Journal of Computer Science.
28. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
29. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
30. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
31. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
32. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
33. Jagadeesh, S., & Soundappan, R. S. (2014). Survey on knowledge discovery in speech emotion detection. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4476-4481.
34. Murugeshwari, B., & Sujatha, R. (2014). Preservation of Privacy for Multiparty Computation System with Homomorphic Encryption. International Journal of Emerging Technology and Advanced Engineering, 4(3), 530-535.
35. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
36. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
37. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
38. G. Vimal Raja, K. K. Sharma (2015). Applying Clustering technique on Climatic Data. Envirogeochimica Acta, 2(1), 21-27.
39. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
40. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
41. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
42. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
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