Show HN: Lance – Open lakehouse format for multimodal AI datasets (github.com)

Lance has introduced an open lakehouse format tailored for multimodal AI, enabling high-performance data handling essential for modern AI workflows. This format supports not only vector search and full-text search but also provides robust capabilities for random access and feature engineering. With compatibility across several popular data frameworks like Pandas and Spark, Lance positions itself as a versatile solution for managing diverse datasets, including images, videos, audio, and text. This development is significant for the AI/ML community as it directly addresses the limitations of traditional lakehouse formats, which often struggle with AI-specific requirements. Lance offers features such as accelerated hybrid search, 100x faster random access than Parquet, and efficient data evolution without extensive rewrites, making it ideal for large-scale machine learning tasks. By integrating these AI-centric features into a single format, Lance enhances the development cycle for machine learning, fostering quicker iterations and more effective data utilization across various stages from exploration to deployment.