来源：市场资讯 （来源：美股情报站） Snowflake被视为云数据仓库领域的标杆企业，起源于云数据仓库和SQL分析，并逐渐向AI原生数据服 务平台延伸。公司在AI领域的关键位置在于AI软件技术栈中的数据与治理层，及AI运维层与AI运行接 入层。其平台云原生体验通过Cortex等应用将AI模型调用及检索、分析功能纳入，同时原生应用框架允 许第三方将AI/ML应用直接作为云原生应用推广。 市场普遍将Databricks视为Snowflake的主要竞争对手。Databricks最新估值约1000亿美元，即便 Snowflake股价大涨后，其市值仍约为800亿美元，反映出与竞对相比仍有增长潜力。 云数据仓库领域的领军者Snowflake预期在2026财年下半年继续受到人工智能热潮的推动，以强劲业绩 增长令华尔街分析师纷纷调升目标股价并坚守乐观评级。 在公布第二财季业绩和未来展望后，Snowflake股价在随后的美股交易日中单日飙升逾20%。凭借全球 范围内对AI布局的空前热潮，Snowflake股价自年初上涨了55%，明显超越标普500和纳斯达克100指数 的表现。 目前，全球企业和政府部门在人工智能基础设施和AI ...

Core Viewpoint - The article discusses the transformative impact of Agentic AI on the software ecosystem, particularly how traditional data warehouses are being challenged by new architectures that prioritize semantic and responsive data handling over structured querying [1][3][34]. Group 1: Industry Changes - Snowflake's recent CEO change signals a paradigm shift in the data warehouse landscape, moving from a focus on traditional data warehousing to an AI-first approach [2][3]. - The emergence of Agentic AI, which acts as an intelligent agent capable of understanding and executing tasks, raises questions about the relevance of traditional decision support systems designed for human users [4][5][22]. - The traditional data warehouse, once a critical asset for enterprises, may become merely a repository of raw data for these intelligent agents, diminishing its value [6][30]. Group 2: Evolution of Data Architecture - The evolution of data warehouse architecture has seen significant milestones, from Bill Inmon's foundational concepts in the 1970s to the rise of cloud-native solutions like Snowflake in 2015 [9][18]. - The article outlines how the introduction of big data technologies and cloud computing has reshaped the data landscape, leading to a decline in the dominance of traditional MPP architectures [16][17]. - The concept of Agentic Data Stack is introduced as a new architecture that integrates data and semantics, designed to meet the needs of AI agents [36][39]. Group 3: Future Implications - The future of data warehouses will likely involve a shift from human-centric designs to architectures that cater to AI agents, fundamentally altering how data is stored, processed, and utilized [30][31]. - The article predicts that as Agentic AI becomes more prevalent, the roles of various business functions will be redefined, with agents taking over tasks traditionally performed by humans [25][27]. - The transition to Agentic Data Stack is expected to reduce the construction cycle of data warehouses significantly, enabling real-time data access and processing capabilities [39][40].