Lance and Iceberg for Multimodal AI Data

This article discusses the complementary roles of Apache Iceberg and LanceDB in building a multimodal AI data architecture. Iceberg is optimized for analytical workloads like columnar scans and SQL aggregations, while Lance handles random-access retrieval needed for ML training, such as fetching similar images via vector indexes. It covers the technical mismatch between these patterns, how Lance's on-disk IVF-PQ index enables efficient random access, and practical workflows for versioning training datasets and fine-tuning. The article also compares Lance with dedicated vector databases and explores production deployment options.