Optimizing Data Management Pipelines With Artificial Intelligence Challenges And Opportunities
Keywords:
AI optimization, data pipelines, automation, real-time analytics, machine learning, data integration, ethical AI, intelligent processing.Abstract
The purpose of this paper is therefore to highlight how AI is key in both improving data pipelines and dealing with the continuing rise in data in the contemporary world. Machine learning and other forms of artificial intelligence, prediction, and self-automation help the
organization swap simple data analysis for process-based intelligent, adaptive, real-time decision-making gears.
References
V. Raman and J. M. Hellerstein, "Potter’s Wheel: An Interactive Data Cleaning System," Proc. 27th Int. Conf. Very Large Data Bases (VLDB), Rome, Italy, 2001, pp. 381-390.
CloudDataInsights, "How to Overcome Top Data Pipeline Challenges," 2022. [Online]. Available: https://www.clouddatainsights.com/top-data-pipeline-challenges-and-whatcompanies-need-to-fix-them/
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Published
2024-12-02
How to Cite
Anant Agarwal ,Ridhi Deora ,Sumit Abhichandani ,Rasik Borkar. (2024). Optimizing Data Management Pipelines With Artificial Intelligence Challenges And Opportunities . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 2615–2636. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2177
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