AI-Driven Big Data Analytics: Scalable Architectures and Real-Time Processing

An Li

European Journal of AI, Computing & Informatics, 2025, vol. 1, issue 1, 33-41

Abstract: With the rapid growth of big data, the integration of Artificial Intelligence (AI) has become crucial for enhancing the scalability and real-time processing capabilities of data systems. This paper explores how AI-driven models, including machine learning, deep learning, and reinforcement learning, are revolutionizing big data analytics by improving data processing efficiency and enabling immediate, data-driven decision-making. It discusses the role of scalable architectures like cloud computing, distributed systems, and edge computing in supporting AI's capabilities, and how platforms such as Kafka and Flink facilitate real-time data stream processing. Additionally, this study examines the challenges of data quality, model scalability, and ethical concerns in AI-powered big data systems. The paper concludes with insights on future trends, such as AutoML, TinyML, and federated learning, which promise to further enhance the integration of AI and big data in real-time analytics.

Keywords: artificial intelligence; big data; real-time processing; scalability; machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://pinnaclepubs.com/index.php/EJACI/article/view/39/42 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dba:ejacia:v:1:y:2025:i:1:p:33-41

Access Statistics for this article

More articles in European Journal of AI, Computing & Informatics from Pinnacle Academic Press
Bibliographic data for series maintained by Joseph Clark ().