Navigating the Landscape of Big Data Methods: A Survey through the Lens of Deep Learning

The era of Big Data has ushered in an unprecedented deluge of information, characterized by its massive volume, diverse variety, high velocity, and inherent veracity challenges. Traditional data processing and analysis techniques often falter in the face of such complexities. Deep Learning, with its capacity to discern intricate patterns and representations from raw data, has emerged as a promising tool to navigate this landscape. This survey article provides a comprehensive overview of the methods employed in the realm of Big Data, systematically categorized based on the stage of data processing they address (preprocessing, storage, or processing & management), the type of learning involved (supervised, unsupervised, or reinforcement), the specific data characteristics they tackle (volume, variety, velocity, or veracity), and their application areas. We delve into the strengths and limitations of each method, highlighting their suitability for different Big Data scenarios. Furthermore, we explore the challenges and future trends in applying Deep Learning to Big Data, emphasizing the need for innovative solutions to harness its full potential. By offering a structured classification and insightful analysis, this survey serves as a valuable resource for researchers and practitioners seeking to understand and leverage the synergy between Deep Learning and Big Data.