The article examines the evolution of data lake storage acceleration, compares various solutions, and explains how metadata, read/write, and end‑to‑end optimizations enable scalable, cost‑effective AI and big‑data workloads in cloud‑native environments.
This article introduces Delta Lake 3.1, detailing its release background, the addition of Deletion Vector to Update and Merge commands, metadata‑driven count/min/max optimizations, the Universal Format for cross‑engine compatibility, and a comparative evaluation with Iceberg and Hudi.
GooseFS, Tencent Cloud’s distributed caching system for storage‑compute separation, links compute frameworks to underlying storage (COS, CHDFS, COSN) and boosts big‑data and AI workloads by 2‑10× through transparent acceleration, robust master‑worker architecture, Raft‑based HA, tiered caching, and metadata optimizations, delivering up to 50% cost savings and 29% faster compute jobs.
