Real-Time Data Analytics Integration: A Technical Deep Dive

Real-time data analytics integration represents a transformative approach to processing and utilizing data in modern organizations. This comprehensive article of real-time analytics systems explores the architectural foundations, implementation considerations, and industry applications that define successful deployments. The content delves into event-driven architectures, core components, and best practices that enable organizations to harness immediate insights from their data streams. The discussion encompasses performance optimization strategies, data quality frameworks, and fault tolerance mechanisms essential for robust real-time analytics systems. Industry-specific applications across financial services, healthcare, manufacturing, and retail sectors demonstrate the practical impact of real-time analytics in driving operational improvements and customer value. The exploration extends to emerging trends, including edge computing integration, AI/ML model deployment at the edge, quantum computing applications, and 5G network utilization, highlighting the evolving landscape of real-time analytics capabilities and their potential to reshape data processing paradigms across industries.

Authors

Institutions

Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-02-10
DOI
https://doi.org/10.32628/cseit251112253
Primary Topic
Real-Time Systems Scheduling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real-Time Data Analytics Integration: A Technical Deep Dive

Dileep Kumar Siripurapu
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Real-Time Systems Scheduling
article

Real-Time Data Analytics Integration: A Technical Deep Dive

Dileep Kumar Siripurapu
article en

Abstract

Real-time data analytics integration represents a transformative approach to processing and utilizing data in modern organizations. This comprehensive article of real-time analytics systems explores the architectural foundations, implementation considerations, and industry applications that define successful deployments. The content delves into event-driven architectures, core components, and best practices that enable organizations to harness immediate insights from their data streams. The discussion encompasses performance optimization strategies, data quality frameworks, and fault tolerance mechanisms essential for robust real-time analytics systems. Industry-specific applications across financial services, healthcare, manufacturing, and retail sectors demonstrate the practical impact of real-time analytics in driving operational improvements and customer value. The exploration extends to emerging trends, including edge computing integration, AI/ML model deployment at the edge, quantum computing applications, and 5G network utilization, highlighting the evolving landscape of real-time analytics capabilities and their potential to reshape data processing paradigms across industries.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(1)
McPhee Research (United States) (US)
Openalex Percentile: Top 98%
Real-Time Systems Scheduling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.