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 why 74% of AI projects fail due to feedback gaps and data silos, explains how the open‑source Lance format addresses these issues with unified multimodal storage, outlines a layered Lance‑on‑Ray architecture, and details three real‑world practices—implicit feedback loops, GPU‑accelerated self‑evolution, and semantic knowledge‑graph evolution—to boost R&D efficiency.
This article details a step‑by‑step guide for constructing a high‑performance multimodal data pipeline—covering video segmentation, duration filtering, frame extraction, safety and aesthetic scoring, and caption generation—using Alibaba Cloud PAI, Paimon, DataJuicer, and distributed frameworks like Ray and Daft, with real‑world performance metrics.
This article analyzes how the Daft‑Ray‑Lance stack tackles the challenges of multimodal AI workloads by offering a high‑performance Rust engine, adaptive back‑pressure, seamless Ray‑based distributed scheduling, and a storage format optimized for random access, vector indexing, and zero‑copy schema evolution, complete with benchmark comparisons and practical deployment guidance.
This article details a scalable, distributed pipeline that uses LAS AI Data Lake, Daft on Ray, and advanced video‑processing techniques—scene detection, splitting, frame sampling, filtering, and caption generation—to transform tens of millions of hours of robot‑captured video into high‑quality, searchable semantic data while dramatically boosting CPU and GPU utilization.
This article explores how the LAS team's AI‑driven data lake solution, built on Daft for lake computing and Lance for lake storage, tackles the emerging challenges of multimodal data handling, offering faster I/O, heterogeneous CPU‑GPU scheduling, and seamless integration for AI workloads.
