Data Engineering Evolution: Embracing Cloud Computing, Machine Learning, and AI Technologies

Authors

  • Jawaharbabu Jeyaraman TransUnion – USA Author
  • Muthukrishnan Muthusubramanian Discover Financial Services-USA Author

DOI:

https://doi.org/10.60087/jklst.vol1.n1.p89

Keywords:

Data engineering, Cloud computing, Machine learning, Artificial intelligence, Data management, Data processing, Data analysis

Abstract

The evolution of data engineering has been greatly influenced by advancements in cloud computing, machine learning (ML), and artificial intelligence (AI) technologies. This paper explores the intersection of these domains and discusses how data engineering practices have adapted to leverage the capabilities offered by cloud platforms and intelligent systems. It provides insights into the integration of ML and AI techniques into traditional data management processes, highlighting the challenges and opportunities associated with this evolution. Additionally, the paper discusses the impact of these technologies on data processing, storage, analysis, and decision-making, emphasizing the need for organizations to embrace these innovations to stay competitive in today's data-driven landscape

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References

Nakamoto, S. Bitcoin: A peer-to-peer electronic cash system; Manubot: 2019.

Mantelero, A. J. C. L.; Review, S., AI and Big Data: A blueprint for a human rights, social and ethical impact assessment. 2018, 34 (4), 754 - 772.

Singh, M. P.; Huhns, M. N. J. I. E., Automating workflows for service order processing: Integrating AI and database technologies. 1994, 9 (5), 19 - 23.

Kowalski, R., AI and software engineering. In Artificial Intelligence and Software Engineering, Ablex Publishing: 1991; pp 339 - 352.

Artikis, A.; Bamidis, P. D.; Billis, A.; Bratsas, C.; Frantzidis, C.; Karkaletsis, V.; Klados, M.; Konstantinidis, E.; Konstantopoulos, S.; Kosmopoulos, D. In Supporting tele-health and AI-based clinical decision making with sensor data fusion and semantic interpretation: The USEFIL case study, International workshop on artificial intelligence and NetMedicine, 2012; p 21.

Yao, X. J. P. o. t. I., Evolving artificial neural networks. 1999, 87 (9), 1423 - 1447.

Basheer, I. A.; Hajmeer, M. J. J. o. m. m., Artificial neural networks: fundamentals, computing, design, and application. 2000, 43 (1), 3 - 31.

Zhang, G.; Patuwo, B. E.; Hu, M. Y. J. I. j. o. f., Forecasting with artificial neural networks:: The state of the art. 1998, 14 (1), 35 - 62.

Bratko, I., Prolog programming for artificial intelligence. Pearson education: 2001.

Luger, G. F., Artificial intelligence: structures and strategies for complex problem solving. Pearson education: 2005.

Bond, A. H.; Gasser, L., Readings in distributed artificial intelligence. Morgan Kaufmann: 2014.

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Published

30-04-2023

How to Cite

Jeyaraman, J., & Muthusubramanian, M. (2023). Data Engineering Evolution: Embracing Cloud Computing, Machine Learning, and AI Technologies. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 1(1), 85-89. https://doi.org/10.60087/jklst.vol1.n1.p89

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