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

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  • Li, An

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.

Suggested Citation

  • Li, An, 2025. "AI-Driven Big Data Analytics: Scalable Architectures and Real-Time Processing," European Journal of AI, Computing & Informatics, Pinnacle Academic Press, vol. 1(1), pages 33-41.
    • Handle: RePEc:dba:ejacia:v:1:y:2025:i:1:p:33-41
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    File URL: https://pinnaclepubs.com/index.php/EJACI/article/view/39/42
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