Real-Time Analytics in the Cloud: Innovations in Stream Processing

Author(s)	Rohini Isarapu, Sharathchandra Gowda
Country	United States
Abstract	This comprehensive article examines the evolution and current state of stream processing technologies in cloud environments, focusing on real-time analytics innovations. The article explores key technological advancements in stream processing frameworks, including Apache Kafka, Apache Flink, and Google Cloud Dataflow, while analyzing their impact on operational efficiency and decision-making processes. The article delves into the integration of machine learning with stream processing, addressing technical challenges such as data consistency and performance optimization. Furthermore, it investigates various windowing strategies and implementation considerations crucial for modern stream processing systems. The article also highlights emerging trends in edge computing integration, automated scaling mechanisms, and complex event processing capabilities, providing insights into the future direction of stream processing technologies.
Keywords	Keywords: Stream Processing, Real-time Analytics, Cloud Computing, Machine Learning Integration, Edge Computing
Field	Computer
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-31
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.34276
Short DOI	https://doi.org/g82ggj

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International Journal For Multidisciplinary Research