A deep dive into how table formats handle version management for ML/AI experimentation, and how Lance unifies branching, tagging, and shallow clone on top of its multi-base architecture.
A tour of Lance's file path design, and how Lance’s new multi-base layout enables multi-location datasets (such as Uber’s multi-bucket setup) with minimal metadata rewrites.
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.
Announcing the formal governance structure for the Lance community, establishing clear pathways for contribution and leadership with a three-tier system of contributors, maintainers and PMC.
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.
Access and manage your Lance tables in Hive, Glue, Unity Catalog, or any catalog service using Lance Namespace with the latest Lance Spark connector.
Explore the future of open source table formats: apache iceberg and lance with practical insights and expert guidance from the LanceDB team.