A practical definition of multimodal complexity, and how LanceDB’s Multimodal Lakehouse is built to address these challenges.

We’re excited to announce that the core Rust SDK and the Python and Java binding SDKs are graduating to version 1.0.0, alongside a new, community-driven release strategy.

A comparison of where Iceberg and Lance sit in the modern lakehouse stack. We highlight emerging architectures that are bridging the worlds of analytics and AI/ML workloads using these two formats, while being built on the same data foundation.

The 2.1 file version is now stable, learn what that means for you and what's coming next.

Introducing RaBitQ quantization in LanceDB for higher compression, faster indexing, and better recall on high‑dimensional embeddings.

Build semantic video recommendations using TwelveLabs embeddings, LanceDB storage, and Geneva pipelines with Ray.

Learn how to build real-time multimodal AI analytics by integrating Apache Fluss streaming storage with Lance's AI-optimized lakehouse. This guide demonstrates streaming multimodal data processing for RAG systems and ML workflows.

Learn how to productionalize AI workloads with Lance Namespace's enterprise stack integration and the scalability of LanceDB and Ray for end-to-end ML pipelines.

Learn how to build scalable feature engineering pipelines with Geneva and LanceDB. This demo transforms image data into rich features including captions, embeddings, and metadata using distributed Ray clusters.