Apache Hudi 1.1 introduces major enhancements for data lakehouse capabilities, focusing on performance and multi-format support. The highlight of this release is the pluggable table format framework, allowing integration with other formats like Apache Iceberg and Delta Lake. This design helps organizations avoid vendor lock-in and choose the right format for their needs. The new architecture maintains Hudi’s core features like transaction management and indexing while enabling compatibility across different storage formats. The indexing subsystem sees significant updates, particularly with the introduction of the partitioned record index. This variant improves lookup efficiency by leveraging partition information, making it suitable for datasets where uniqueness is required only within a partition. Another notable enhancement is the partition-level bucket index, which allows varying bucket counts across partitions based on regex rules. This flexibility is especially beneficial for time-series data, where partition sizes can fluctuate over time. Hudi 1.1 also optimizes metadata table operations, resulting in faster performance for both reads and writes. New features like the HFile block cache and Bloom filters significantly speed up data lookups. The update includes a native HFile writer, reducing the dependency on HBase and streamlining the overall package. These improvements not only enhance operational efficiency but also lay the groundwork for more advanced features in future releases.