Multimodal AI workloads (audio, video, images, documents) break traditional data engines. The team at Daft released a benchmark of their engine against Spark and Ray Data across four real-world multimodal workloads:
Audio transcription (113K files with Whisper)
Document embedding (10K PDFs)
Image classification (803K ImageNet images with ResNet18)
Video object detection (1K videos with YOLO11n)
Results: Daft ran 2-7x faster than Ray Data and 4-18x faster than Spark, completing jobs reliably without failures. On the heaviest workload (video detection), Spark took over 3.5 hours while Daft finished in under 12 minutes.
The benchmark code and logs are fully open-sourced for reproducibility. If you're building multimodal AI pipelines, this is worth checking out.
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