StarRocks now fully supports Paimon 2.0, enabling multimodal lakehouse workloads where columnar scans, scalar/vector/full-text indexes, and BLOB fetches coexist in one SQL execution framework, backed by Row Tracking and Data Evolution for incremental feature backfill.
StarRocks now fully supports Paimon 2.0, integrating columnar analytics with vector, full-text, and scalar indexes into a single SQL execution framework for multimodal lakehouse workloads, enabling unified data management, retrieval, and relational analysis on object storage without data movement.
Apache Paimon 1.4 redefines itself as an AI multimodal data lake by introducing row tracking, data evolution, Blob and Vector tables, Variant shredding, and Lumina‑BTree global indexing, each explained with concrete examples, configuration flags, and storage layouts that illustrate how the new capabilities enable unified storage and efficient retrieval of diverse data types.
Apache Paimon 1.3 introduces incremental clustering for 150x faster append-table queries, a pure Python SDK 95x faster than its JVM-based predecessor, data evolution cutting wide-table storage by 99.4%, a virtual file system for unified cross-engine access, and upcoming vector storage for AI workloads.
