OPPO tackles explosive multimodal data growth by unifying metadata with Gravitino and boosting I/O performance using the open‑source Curvine cache, delivering a four‑layer data‑lake architecture that resolves data islands, metadata chaos, and bandwidth bottlenecks while achieving near‑commercial query speeds.
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.
The article analyzes how Tencent Cloud's TBDS platform tackles the AI era's multimodal data lake challenges through a native storage format (Lance), elastic Ray‑based compute, standardized metadata with Gravitino, and automated governance via Lakekeeper, citing architecture details, performance numbers, and real‑world deployments.
The article explores how Volcano Engine’s multimodal data lake architecture addresses the storage, compute, and management challenges of AI agents by introducing new formats like Lance, upgrading engines such as Spark and Daft, and providing unified tools for processing, versioning, and querying massive multimodal datasets.
