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.
Our October newsletter highlights Semantic.Art, Lance File 2.1, RaBitQ Quantization, upcoming events, latest product and community updates.
Our September newsletter welcomes new Lancelot members, highlights TwelveLabs semantic video recommendations, Cognee’s AI memory layer, and shares the latest product and community updates.
The 2.1 file version is now stable, learn what that means for you and what's coming next.
A lightweight open source web UI for exploring Lance datasets, viewing schemas, and browsing table data with vector visualization support.
How Cognee uses LanceDB to deliver durable, isolated, and low-ops AI memory from local development to managed production.
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.
Our September newsletter highlights LanceDB powering Netflix's Media Data Lake, a case study on CodeRabbit's AI-powered code reviews, and updates on Lance Namespace and Spark integration.
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.
