Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.
The article analyzes Palantir's 2026 Q1 surge and argues that as large‑model capabilities become cheap commodities, true competitive advantage now lies in deep ontology‑based infrastructure that makes AI outputs trustworthy in high‑risk enterprise scenarios.
The article explains that most AI agents achieve only 80‑90% accuracy in read‑only tasks and cannot handle core production decisions, then details Baidu’s “Sheng Suan” platform which uses a three‑layer business ontology and system‑engineered sandbox, audit, and simulation features to enable agents to execute write operations, citing three real‑world cases where decision latency dropped from months to minutes and accuracy exceeded 95%.
The article argues that in the era of ever‑larger language models, enterprises lack a unified, computable, and evolvable semantic structure, and that ontology—recast as a semantic operating system—provides the necessary skeleton, guardrails, and actionable knowledge to make AI systems truly understand and execute business processes.
The article explains how ContextSearch transforms enterprise search from simple document retrieval into an Agentic, multi‑source, runtime‑driven context layer that can understand constraints, gather evidence, verify results, and continuously evolve through trace‑backed optimization.
This article details Hisense's journey to create an AI‑ready multimodal data platform, covering the challenges of integrating diverse business systems, the shift from a Hadoop‑based architecture to a cloud‑native data lake, the JuData governance and development platform, and six practical scenarios that demonstrate unified ingestion, metadata management, rule‑based quality control, intelligent asset retrieval, and future AI‑driven DataOps capabilities.
The rise of AI agents as data consumers forces a fundamental shift in data infrastructure design, requiring unified metadata control, a robust semantic layer, and a governed agent access framework to replace traditional human‑centric RBAC models and ensure secure, auditable operations.
The article analyzes cutting‑edge AI search and recommendation technologies—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB—detailing their architectural evolution, multi‑modal retrieval strategies, GPU acceleration gains, and measured performance improvements.
As AI agents begin generating hundreds of lines of code and rapidly iterating database changes, traditional DBA manual review becomes obsolete; NineData’s AI‑native cloud‑native platform leverages intelligent SQL generation, automated review, and ChatDBA to boost development efficiency tenfold, cut repetitive work by 90%, and deliver high‑availability, secure, multi‑cloud data management at enterprise scale.
The article analyses how memory has become the critical limitation for AI agents, details the MemOS framework’s five‑layer architecture that fuses model‑driven and application‑driven approaches, presents cloud service usage surging over 200%, and explains how these advances address scalability, privacy, and performance challenges in enterprise deployments.
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.
