A Novel Method for Video Manipulation and Compilation Using Big Data Technologies

With the rise of multimedia applications, the demand for faster and scalable video processing systems has grown significantly. Traditional video processing methods face challenges such as high computational complexity, difficulty in scaling, and real-time performance limitations. This paper explores a solution that combines Apache Spark’s distributed computing capabilities with GPU acceleration using CUDA to improve video processing efficiency. By leveraging the Spark partitioning model alongside GPU parallelism, we enhance key tasks like frame extraction, dynamic filtering, and object detection. Experimental results show that this approach improves processing speed by 40% compared to CPU-based methods. This research provides a practical solution for large-scale, real-time video processing with a focus on entertainment, surveillance, and media analytics industries, while also discussing ways to further optimize large video data handling.