Deep Learning Approaches for Big Data Analytics in Distributed Systems

Abstract Big Data has become a fundamental component of modern computing systems due to the exponential growth of digital information generated from diverse sources such as social media platforms, IoT devices, cloud infrastructures, and enterprise applications. Traditional data processing methods are no longer sufficient to manage the massive volume, high velocity, and wide variety of this data. Deep Learning (DL), a specialized branch of Machine Learning, has demonstrated exceptional capability in identifying complex patterns and extracting valuable insights from large-scale datasets. This research paper examines Deep Learning techniques for Big Data analytics within distributed computing environments. It presents key architectures, major challenges, widely used frameworks, and performance optimization strategies. Furthermore, the study emphasizes how distributed platforms such as Apache Spark and Apache Hadoop support scalable model training and enable efficient real-time analytics, making large-scale Deep Learning implementation practical and efficient. Keyword: - Big Data, Deep Learning, Distributed Computing, Apache Spark, Apache Hadoop, Scalability, Parallel Processing, Large-Scale Data Analytics.