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

Shashank Reddy Beeravelly

doi:10.32628/CSEIT241061113

Authors

Shashank Reddy Beeravelly University of Houston Clear Lake, USA Author

DOI:

https://doi.org/10.32628/CSEIT241061113

Keywords:

Real-time Analytics, AI-Driven Optimization, Stream Processing, Cloud-Native Architecture, Predictive Analytics

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.

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