As data scales grow, open-source table formats like Apache Iceberg and the emerging Lance format are essential for efficient data lake management, each offering unique advantages for different workloads. Apache Iceberg has become a leader due to its strong integration and capability to handle large-scale structured data with features like ACID transactions and schema evolution, but it faces challenges in metadata overhead and lacks support for multimodal data types and efficient random access. On the other hand, Lance is designed to optimize modern data applications, particularly for AI and ML workloads, by offering efficient metadata management, native multimodal data support, and low-latency random access, making it suitable for AI-driven applications. While Iceberg serves as a foundational format for data exchange and interoperability across major query engines, Lance facilitates efficient column appends and high-performance operations critical for real-time AI and ML workflows. The future of data lake table formats will likely see these systems coexist and complement each other, catering to the evolving needs of hybrid analytics and AI-driven architectures, with collaborative developments enhancing their interoperability.