The rapid evolution of table formats like Iceberg, Delta, and Hudi has sparked questions about why new formats, such as Lance, are necessary. Lance addresses specific challenges in handling modern, large-scale machine learning workloads with its two-dimensional storage layout, allowing efficient schema evolution by adding new columns without rewriting existing data. The blog discusses the "curse of wide data," where datasets grow horizontally as more features are added, and highlights Lance's capability to manage this complexity through strategic data file management. Additionally, Lance emphasizes the importance of indices and random access to improve performance, especially in scenarios requiring fast data retrieval and updates. While traditional table formats often rely on primary indices, Lance integrates diverse indices to handle a variety of search and update tasks efficiently. The blog also notes the importance of parallel processing for handling big data operations, an area where Lance seeks to excel by offering a database-like API that supports distributed processes. Future goals for Lance include improving current implementations, enhancing integration support with other table formats and data systems, and developing new types of indices to further optimize its performance.