Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
The world of continuously operating, low-latency data processing and analytics across diverse areas (finance, healthcare, e-commerce, IoT) is already supported by real-time data systems that have become a staple of the contemporary digital infrastructure. This review explores the end-to-end architecture and technologies that support the management of real-time data systems, data ingestion, through analytics. The focus is on principles of system design, models of processing, performance analyses, and new frameworks. Based on comparative analysis and the empirical outcomes, the review points out the existing capabilities, trade-offs in the performance, and operational limitations. The main problems of consistency, latency, scalability, and resource management are solved, and the ideas of future research are described.
"Building Real-Time Data Systems at Scale: From Ingestion to Analytics", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.b667-b672, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506180.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator