Beyond traditional analytics: Exploring hybrid deep learning architectures for scalable data analysis

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The increased sophistication and size of current data sets have made conventional analytics methods ineffective and called for effective strategies to work through large data sets. Hybrid deep learning architectures are examined in this paper as a revolutionary approach to Big Data analysis. Due to incorporating features obtained from various deep learning frameworks involving Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and unique Transformer-based architectures, these structures expound improved performances over several analytical tasks. The authors assess the performance of several hybrid models compared to conventional approaches and single deep learning (DL) methods using suitable parameters like accuracy, time of processing, and scalability. The trends are also done in a year-by-year evolution to show the development of technology, whereas comparative bar graphs are used to show the development of capabilities. Outcomes demonstrate that hybrid architectures are superior to customary methods while having superior scalability and functionality across additive datasets. The contribution of this paper is that it provides a critical analysis of the use of hybrid architectures and the implications of their current deployment and evolution for the establishment of the next generation of analytical systems.