Why Multimodal Data Needs a Better Lakehouse?
Data lakes only handle tabular data, search engines just work with vectors, and neither work well with multimodal data. Researchers using today's infrastructure face more complexity, higher cost, and slower progress. It's time for a new foundation.
We explore the challenges and limitations of current data lakehouse architectures in handling multimodal data, crucial to modern AI workloads:
Current lakehouses lack native support for multimodal data like images, audio, and video.
Building successful AI depend on seamless handling of diverse data types and multiple workloads.
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.
Enterprise-Grade Compliance
Safety and security guaranteed for your data.
Designed for Multimodal Data. Built for Enterprise Scale.
LanceDB is the first open-source AI-Native Multimodal Lakehouse.
