Bodo's July release introduces significant updates to its DataFrame library, focusing on enhancing interaction with open-table formats like Iceberg and Parquet, and making database-grade analytics more accessible. Key features include native support for writing Iceberg and Parquet tables, enabling seamless integration of computation-heavy pipelines into data lakes without relying on tools like Spark, thus reducing latency and enhancing immediate query capabilities. The release also improves the GroupBy functionality, allowing for in-process complex aggregations that scale with system cores, and expands the Pandas API surface for a more familiar experience. Quality-of-life enhancements include easier column operations and relaxed Python dependency constraints, ensuring compatibility with modern Python versions and integration into existing CI pipelines. Overall, Bodo 2025.7 aims to bridge the gap between high-speed DataFrame computation and production-grade data lake operations, making it an essential tool for building efficient and versatile data pipelines.