Big data processing plays a pivotal role in the development and efficiency of Generative Artificial Intelligence (AI) systems. These AI models, which include examples like language models, image generators, and music composition tools, require substantial amounts of data to learn and generate new, coherent, and contextually relevant outputs. The intersection of big data processing techniques with generative AI poses both opportunities and challenges in terms of scalability, data diversity, and computational efficiency.
This paper explores the methodologies employed in processing large datasets for training generative AI models, emphasizing the importance of data quality, variety, and preprocessing techniques. We discuss the