Deep Learning for Big Data: A Survey of Architectures,Distributed Frameworks, and Emerging Challenges

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Big data has become a defining feature of moderncomputing, and deep learning has emerged as the primary toolfor extracting predictive value from such large-scale, high-velocity, heterogeneous data. This survey reviews theintersection of deep learning and big data along three axes: theneural architectures used to model large-scale data, thedistributed computing frameworks (notably those built onApache Spark) that make training such models tractable, and thepersistent challenges of scalability, data quality, privacy, andinterpretability that constrain real-world deployment. Wefurther examine emerging responses to these challenges,including federated learning for privacy-preserving distributedtraining, explainable AI (XAI) for large-scale models, andedge/TinyML approaches for resource-constrained deployment.The survey closes with open research directions, includingcommunication-efficient distributed training, robustness to non-IID data, and standardized benchmarking for big data deeplearning systems