Multimodal AI workloads, which involve processing diverse data types like text, images, audio, and video, are increasingly challenging current infrastructure capacities, requiring systems capable of managing high-throughput pipelines and efficient CPU and GPU scheduling. Ray Data, a data processing engine designed for these workloads, offers a streaming batch execution model that optimizes resource utilization and reduces costs, alongside ecosystem integrations with AI projects like vLLM and PyArrow. It scales CPU and GPU workers independently and supports fault tolerance and autoscaling, allowing code to run unchanged across various scales. Recent benchmarks compared Ray Data to Daft, a distributed DataFrame library, revealing Ray Data to be generally faster and more efficient, particularly in leveraging GPU utilization and reducing CPU starvation. The benchmarks highlighted Ray Data's superior performance, especially in large-scale workloads, where it achieved up to 7x faster processing than alternatives. Ray Data's design for cluster heterogeneity enables it to maximize GPU efficiency in pipelines with substantial CPU steps, and its ongoing optimizations promise further advancements. While Daft is acknowledged for its efficiency in low-resource settings, Ray Data demonstrates enhanced performance with larger instance types, encouraging users to evaluate both systems based on specific needs.