The article examines the challenges of multimodal data in modern lakehouses and presents a three‑tool stack—Gravitino, Daft, and Lance—that provides unified metadata, distributed multimodal compute, and high‑performance storage, while detailing security governance, integration paths, and future directions.
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.
This article collection examines Tencent Cloud’s Iceberg batch‑stream integration, AI‑driven game data governance, Apache Gravitino unified metadata and lineage, Xiaohongshu’s multimodal data‑lake evolution, and Volcano Engine’s Data+AI multimodal lake, highlighting architectures, techniques, performance gains, and practical implementations.
This article explains how Tencent Cloud's TBDS platform evolves its architecture by adopting Apache Gravitino as a unified metadata lake, detailing the challenges of legacy versus new lakehouse designs, storage and compute separation, unified data access, permission management, and the resulting benefits for big‑data and AI workloads.
This article introduces Tencent's Big Data Processing Suite (TBDS) and the open‑source Gravitino project, explaining how they provide a unified metadata service and a comprehensive, extensible permission model to address data and permission islands across heterogeneous Hadoop and MPP ecosystems.
This article introduces Tencent's Big Data Processing Suite (TBDS), discusses challenges of data silos, and presents Gravitino's open‑source unified metadata service and permission model, detailing how it integrates Hadoop, MPP, and various catalog plugins to provide consistent access control across heterogeneous data platforms.
