Advancements in latency reduction for real-time data processing in the cloud

Annam, Deepika (2025) Advancements in latency reduction for real-time data processing in the cloud. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 1370-1376. ISSN 2582-8266

Latency reduction in real-time data processing represents a critical competitive differentiator in contemporary enterprise environments. This comprehensive technical article examines cutting-edge techniques for minimizing processing delays and optimizing performance in cloud-based systems. The digital transformation journey demands instantaneous insights for decision-making, creating unprecedented challenges as data volumes continue to expand exponentially across sectors. Financial services, healthcare, telecommunications, and manufacturing all demonstrate compelling advantages when implementing optimized processing architectures. Advanced techniques including strategic data partitioning, in-memory computing, stream processing frameworks, message broker optimization, and edge computing deployments collectively establish a framework for achieving sub-millisecond responsiveness even at massive scale. The transition from traditional batch processing to continuous real-time analysis fundamentally transforms operational capabilities, enabling organizations to detect anomalies, respond to changing conditions, and deliver personalized experiences with dramatically reduced time-to-insight. As connected device proliferation continues and artificial intelligence capabilities extend to network edges, the importance of latency optimization will only intensify. Organizations mastering these technologies position themselves to capitalize on opportunities that would otherwise be impossible within traditional processing timeframes.