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Abstract
In the era of digital transformation, organizations increasingly rely on real-time analytics to drive decision-making and optimize operations. We examine the key components, including data ingestion, storage, processing, and visualization, and discuss how to leverage cloud-native services to achieve high availability, fault tolerance, and low latency. By evaluating case studies and current industry trends, this paper provides insights into the architectural strategies that enable seamless scaling and efficient resource management, ultimately supporting the real-time analytics needs of modern enterprises. However, the sheer volume, variety, and velocity of Big Data present unique challenges that require specialized tools and approaches to manage and process effectively. Additionally, the diverse formats and structures of Big Data, ranging from text and images to videos and sensor data, add to the complexity. Furthermore, the velocity at which data is generated and needs to be processed—often in real-time—requires solutions that can handle high-speed data streams without compromising performance or accuracy.
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