Live at #DataAISummit from Databricks Databricks AI Research and LanceDB . A joint talk by changhiskhan and Zero Qu Congrats to both teams on the newly announced storage optimized vector search. Now we take billion vector scale to the moon!

Missed Ethan’s talk at @DataCouncilAI 2025? 🎤 He shares how @RunwayML tackles multimodal data challenges—and how LanceDB helps store, query, and retrieve it all efficiently. 🎥 Watch here: youtube.com/watch?v=6kf58x… Ethan's slides: ethanrosenthal.com/keynotes/data_… #LanceDB

Today we’re announcing our $30 million Series A. This round is led by Theory VC with support from CRV , Y Combinator, Databricks, Runway , Zero Prime VC , swiftventurecapital, and more. Your belief in a future powered by multimodal data brings us one step closer to that reality.

We just published a 𝗻𝗲𝘄 𝗯𝗹𝗼𝗴 (lancedb.com/blog/multimoda…) on what the 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 actually does. The Lakehouse is 𝗳𝗼𝗿 𝘁𝗵𝗼𝘀𝗲 working with a mix of text, images, audio, and structured data - 𝘄𝗵𝗼 𝘄𝗶𝘀𝗵 𝘁𝗼 𝗮𝘃𝗼𝗶𝗱 𝘁𝗵𝗲 𝗽𝗮𝗶𝗻 of

q from the audience: "Is Lance the next big thing in data?" answer: "Yes" 👀

swyx dr. jack morris - built by solid db people and hackable (we have a contributor at nomic to it) - used by top ai companies / labs / products for it's nice properties when used in a training loops (e.g. midjourney has been using it since 2023) so probably not going anywhere - feels like the right

🚀 Video from Toronto Machine Learning Society (TMLS) : Character.AI x @LanceDB on building a unified multimodal data lake , a single system for text, audio, video & image retrieval. changhiskhan Ryan Vilim Simpler pipelines, lower infra costs, faster AI dev. 🎥 Watch: youtu.be/8zMeYwR9uQI #AI #LLM

The data prep bottleneck for fine-tuning LLMs is a common challenge. 𝗢𝘂𝗿 𝗻𝗲𝘄 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗠𝗲𝘁𝗮'𝘀 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗞𝗶𝘁 𝗵𝗲𝗿𝗲 𝘁𝗼 𝗳𝗶𝘅 𝘁𝗵𝗮𝘁! It simplifies the entire workflow with a 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝗖𝗟𝗜 for

When building a columnar file reader, it becomes clear that 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻 𝗮𝗯𝘀𝘁𝗿𝗮𝗰𝘁 𝗰𝗼𝗻𝗰𝗲𝗽𝘁. (t.ly/3AyJh) It is the set of rules that determines how every byte of data is stored and accessed on disk. A few months ago,

Join us for our webinar on Apache Spark™ and Lance Spark Connector with Jack Ye (LanceDB) on September 25! 👏 Learn how the Lance Spark Connector enables Apache Spark™ to work with Lance’s AI-native multimodal storage. ✅ We’ll look at how Spark can handle embeddings, images,

This is a big milestone for Lance format. The F3 paper (dl.acm.org/doi/10.1145/37…) verified that Lance has THE fastest random access, essential for search, shuffle, and many other AI workloads. But it incorrectly assumed it was because of lack of compression. With 2.1, we show

1/7 🎨 In a world of infinite scroll, discovering art still feels like searching for a needle in a haystack. With SemanticDotArt, we flipped the question: What if you searched by mood, not just metadata? See how we did this in LanceDB 👇🏽

1/5 LanceDB 🫶🏻 DuckDB We’re happy to announce a new Lance extension for DuckDB! You can simply install this extension in DuckDB and point at your Lance datasets from within a DuckDB CLI or a Python script, while getting 𝗳𝘂𝗹𝗹 𝗦𝗤𝗟 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗼𝗻 𝘁𝗼𝗽 𝗼𝗳

1/5 Large multimodal blobs don’t have to break dataset workflows. Images and videos are often treated as external files, separate from metadata and indexes. Once datasets get large, that split makes exploration, curation, and training painful. Lance changes that on the 🤗

1/6 Here’s a quick example of how to read Hugging Face datasets via LanceDB. Start with opening a LanceDB connection to a dataset on the Hub using the hf:// prefix path.

in case you missed it LanceDB and HF are partnering up to unlock the next generation of large dataset storage on the Hub 🔥 And it's fire! - Supports storing embeddings (and their indexes) directly alongside the data - Vector search / similarity search is built-in - Large

1/4 Branching for ML data shouldn’t slow down production. Iceberg branching → shared metadata bottlenecks. Delta shallow clone → isolation, but loses Git-like UX. We want both. Here’s how Lance unifies branching, tagging, and shallow clone for AI workloads 🧵

1/3 Geospatial support just landed in Lance. And no new storage format work was required. Because Lance is Arrow-native, GeoArrow extension types work out of the box. Geometry columns are preserved end-to-end with zero special casing.

3/3 Huge thanks to: 🙌 Xin Sun (@bytedance) for driving the R-Tree implementation 🙌 Jay Narale (Uber) for the BKD prototype + benchmarking 🙌 Kyle Barron kylebarron.dev on bsky (#GeoArrow / #GeoDataFusion) for ecosystem guidance 🙌 Tim Saucer (#ApacheDataFusion) for helping ensure a clean

🚀想不到这个为OpenClaw定制的增强版LanceDB插件项目发布才三天，都有大佬开始递交pr了。 #OpenClaw github.com/win4r/memory-l…