Announcing Lance SDK 1.0.0: What This Milestone Means for the Community
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.
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From BI to AI: A Modern Lakehouse Stack with Lance and Iceberg
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.
Lance File 2.1 is Now Stable
The 2.1 file version is now stable, learn what that means for you and what's coming next.
LanceDB's RaBitQ Quantization for Blazing Fast Vector Search
Introducing RaBitQ quantization in LanceDB for higher compression, faster indexing, and better recall on high‑dimensional embeddings.
Building Semantic Video Recommendations with TwelveLabs and LanceDB
Build semantic video recommendations using TwelveLabs embeddings, LanceDB storage, and Geneva pipelines with Ray.
Setup Real-Time Multimodal AI Analytics with Apache Fluss (incubating) and Lance
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.
Productionalize AI Workloads with Lance Namespace, LanceDB, and Ray
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.
LanceDB's Geneva: Scalable Feature Engineering
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.
LanceDB WikiSearch: Native Full-Text Search on 41M Wikipedia Docs
No more Tantivy! We stress-tested native full-text search in our latest massive-scale search demo. Let's break down how it works and what we did to scale it.