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This article analyzes the evolution of data‑lake storage acceleration, compares traditional parallel file systems, object‑storage‑based solutions and modern cache‑enabled architectures, and explains how cloud‑native data lakes address scalability, cost, and performance challenges for AI and big‑data workloads.
Baidu’s Canghai Storage unifies metadata, hierarchical namespace, and data layers into a Meta‑Aware, three‑generation architecture that scales to trillions of metadata items and zettabyte‑scale data, using a distributed transactional KV store, single‑machine‑distributed namespace, and online erasure‑coding micro‑services to deliver high performance, low cost, and seamless scalability.
Data lakes have evolved from HDFS to object storage, addressing resource inefficiency, scalability limits, and operational burdens; Baidu’s Data Lake Storage Acceleration 2.0 introduces hierarchical Namespace 2.0, a streaming storage engine, RapidFS caching, and a fully HDFS‑compatible BOS‑HDFS layer to boost performance and support massive AI workloads.
Baidu Intelligent Cloud outlines how its evolving, high-performance infrastructure—featuring rapid 3-minute instance provisioning, over 200 GB bandwidth, elastic computing, specialized storage, and AI-driven MLOps tools—enables AI-native model training and deployment across booming sectors such as automotive and finance, supporting the industry’s shift to AI-centric cloud services.
Baidu Intelligent Cloud’s Qianfan AppBuilder 3.0 offers an enterprise‑grade platform that simplifies large‑model application development by providing high‑accuracy RAG, robust agent scheduling, extensive integration, secure private‑or‑hybrid deployment, and a guided methodology, enabling industries to transform processes, add AI copilots, and create novel capabilities.
The open‑source Yuan 2.0 large model is fully integrated into Baidu’s PaddleNLP, offering quick inference for tasks like code generation, translation, and reasoning, along with efficient distributed training and fine‑tuning features such as Zero Padding optimization, enabling developers to easily deploy and customize the model via simple setup steps and example interactions.
Baidu’s DATAPILOT platform combines natural‑language interaction with GPU‑accelerated Spark‑RAPIDS to turn complex, multi‑table SQL queries into seconds‑fast results, boosting ad‑revenue analysis efficiency by up to five‑fold while reducing infrastructure costs.
Baidu MEG’s TDE‑ClickHouse optimization in the Turing 3.0 ecosystem boosts query speed up to 10×, halves latency, enables billion‑row bulk imports in under two hours, and migrates to a cloud‑native, ZooKeeper‑free architecture supporting 350 k CPU cores, 10 PB storage, and sub‑3‑second responses for 150 k daily BI queries.
The authors redesign Baidu’s Mobile Operations Promotion Platform by separating online business logic from offline warehouse calculations and implementing a custom three‑step online‑offline scheduler that logs operations, orchestrates batch tasks, and dispatches them via TDS, delivering consistent, timely settlement data, reduced errors, and lower maintenance costs.
Baidu Visual Search upgraded its PHP/HHVM stack to Golang, introduced a Backend‑For‑Frontend layer, refactored presentation and system modules with the GDP framework and ExGraph, and built comprehensive monitoring and self‑healing tools, delivering a modular, scalable, and stable AI‑driven search platform.
