The Transition to Cloud-Native Data Analytics: AWS, Azure, and Google Cloud
Keywords:
Cloud-native, AWS, Azure, Google CloudAbstract
Cloud-native data analytics streamlines Big Data administration for data-driven decision-making and competitive advantage. Without on-premises infrastructure, Amazon Web Services, Microsoft Azure, and Google Cloud manage massive datasets flexiblely and cheaply.
These technologies provide effective and intelligent analytics. Data security, regulatory compliance, and legacy system integration remain issues. Each cloud provider offers different storage, analytics, and machine learning, so companies may choose. Cloud-native analytics may improve data processing, agility, and innovation while enhancing security and governance. This transition optimizes data value in the fast-paced digital economy.
References
1. Simmhan, Y., Aman, S., Kumbhare, A., Liu, R., Stevens, S., Zhou, Q., & Prasanna, V. (2013). Cloud-based software platform for big data analytics in smart grids. Computing in Science & Engineering, 15(4), 38-47.
2. Alhamazani, K., Ranjan, R., Jayaraman, P. P., Mitra, K., Wang, M., Huang, Z. G., ... & Rabhi, F. (2014, July). Real-time qos monitoring for cloud-based big data analytics applications in mobile environments. In 2014 IEEE 15th International Conference on Mobile Data Management (Vol. 1, pp. 337-340). IEEE.
3. Chinthapatla, Y. (1924). Harnessing the Power of Big Data with ServiceNow.
4. Jennings, R. (2010). Cloud computing with the Windows Azure platform. John Wiley & Sons.
5. Wilder, B. (2012). Cloud architecture patterns: using microsoft azure. " O'Reilly Media, Inc.".
6. Gandhi, V. A., & Kumbharana, C. K. (2014). Comparative study of Amazon EC2 and Microsoft Azure cloud architecture. International Journal of Advanced Networking & Applications, 117-123.
7. Padhy, R. P., Patra, M. R., & Satapathy, S. C. (2011). X-as-a-Service: Cloud Computing with Google App Engine, Amazon Web Services, Microsoft Azure and Force. com. Com. Int. J. Comput. Sci. Telecommun, 2(9).
8. Bermudez, I., Traverso, S., Munafo, M., & Mellia, M. (2014). A distributed architecture for the monitoring of clouds and CDNs: Applications to Amazon AWS. IEEE Transactions on Network and Service Management, 11(4), 516-529.
9. Rimal, B. P., Choi, E., & Lumb, I. (2009, August). A taxonomy and survey of cloud computing systems. In 2009 fifth international joint conference on INC, IMS and IDC (pp. 44-51). Ieee.
10. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision support systems, 51(1), 176-189.
11. A Vouk, M. (2008). Cloud computing–issues, research and implementations. Journal of computing and information technology, 16(4), 235-246.
12. Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In 2013 international conference on collaboration technologies and systems (CTS) (pp. 42-47). IEEE.
13. Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National science review, 1(2), 293-314.
14. Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information sciences, 275, 314-347.
15. Russom, P. (2011). Big Data Analytic s. TDWI Best Practices report, TDWI Reserarch, 4th Quarter.
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