Advancements in Big Data Analytics: Techniques, Tools, and Applications
Abstract
The exponential growth of data in recent years has necessitated the development of advanced analytics techniques and tools to extract meaningful insights. This review paper explores the evolution of big data analytics, focusing on key methodologies such as machine learning, natural language processing, and real-time analytics. It also examines popular tools like Hadoop, Spark, and TensorFlow, and their applications across industries such as healthcare, finance, and retail. The paper highlights challenges such as data privacy, scalability, and interpretability, while discussing future trends like edge computing and federated learning
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