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The Future of Real-Time Analytics : AI-Driven Insights at Scale

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  • Shashank Reddy Beeravelly

Abstract

Real-time analytics is experiencing a transformative evolution driven by artificial intelligence and cloud computing advancements. This comprehensive article explores cutting-edge developments in AI-powered analytics systems, examining their impact across stream processing engines, query optimization, predictive analytics, and cloud-native architectures. The article investigates how modern systems leverage deep learning, reinforcement learning, and transformer models to enhance processing capabilities, optimize resource utilization, and enable sophisticated predictive insights. Through detailed examination of adaptive stream processing, state management advances, and edge computing integration, this analysis demonstrates how AI-driven approaches are revolutionizing data processing efficiency, scalability, and performance optimization. The article highlights significant improvements in areas such as automated scaling, workload prediction, resource management, and data pipeline optimization, showcasing how these technologies enable organizations to generate actionable insights from real-time data streams while maintaining high performance and cost efficiency.

Suggested Citation

  • Shashank Reddy Beeravelly, 2024. "The Future of Real-Time Analytics : AI-Driven Insights at Scale," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(6), pages 703-712, November.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i6:id:463
    DOI: 10.32628/CSEIT241061113
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241061113
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