Research Study
Why Multimodal Data Needs a Better Lakehouse?
Today’s lakehouses were built for tables, not tensors. It’s time for a data foundation that speaks the language of multimodal AI.
We explore the challenges and limitations of current data lakehouse architectures in handling multimodal data, crucial to modern machine learning and AI workloads:
- Current lakehouses lack native support for unstructured data like images, audio, and video.
- AI and ML workloads depend on smooth handling of diverse, multimodal data types.
- A better lakehouse should unify storage, metadata, and fast access across all modalities.
We propose design principles and potential system enhancements for a new generation of multimodal lakehouses, aiming to bridge the gap between traditional data infrastructure and the needs of large-scale, AI-driven applications.
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