Optimizing Cloud Computing Architectures for Scalable Big Data Analytics in Real Time
Keywords:
Cloud Computing, Big Data, Scalability, Real-time Analytics, Data Architecture, Cloud ArchitecturesAbstract
With the increasing demand for real-time analytics of big data, optimizing cloud computing architectures for scalable, efficient processing is of paramount importance. Cloud platforms offer flexibility and scalability to handle massive datasets, but real-time analytics introduces significant challenges in terms of latency, throughput, and resource management. This paper explores the various architectural strategies, tools, and best practices that can be utilized to enhance the scalability and efficiency of cloud-based systems for big data analytics. Special focus is given to architectural improvements, data storage strategies, and processing frameworks that aid in real-time analysis. The findings of this research demonstrate the importance of selecting the right cloud services and optimizing the infrastructure for big data workloads to achieve high performance in real-time analytics environments.
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Copyright (c) 2025 Luis Ramirez (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
