Core Viewpoint - Traditional SaaS applications like CRM and ERP systems will not be replaced but will evolve to serve as the infrastructure for AI Agents, which will enhance the importance of data definition and interpretation within enterprises [2][10][15] Group 1: The Role of AI Agents - AI Agents will not eliminate traditional software systems; instead, they will necessitate a clearer separation between how tasks are performed and the sources of facts [2][10] - The effectiveness of AI Agents is contingent upon their ability to access and understand the correct data from various systems, highlighting the need for accurate and structured input data [2][9] - The emergence of AI Agents creates significant entrepreneurial opportunities for companies that can help businesses manage and structure their unstructured data [3][10] Group 2: Data Management Challenges - A significant portion of enterprise knowledge (80%) exists in unstructured data, which is becoming increasingly difficult to manage [2] - The complexity of data definitions within organizations leads to discrepancies in key metrics like Annual Recurring Revenue (ARR), complicating the role of AI Agents in providing accurate information [7][11] - The traditional approach of consolidating data into warehouses has only partially succeeded, as operational teams still rely on individual systems for real-time transactions [8][10] Group 3: Evolution of Systems - CRM and ERP systems will transition from user-centric interfaces to machine-oriented APIs, allowing AI Agents to interact with these systems programmatically [12][15] - The core value of enterprise systems lies in their ability to encapsulate chaotic data, which will remain essential despite changes in interface and interaction methods [13][15] - The demand for a clear, authoritative source of truth will only increase as AI Agents become more prevalent in business processes [14][15] Group 4: Future of Data Infrastructure - The combination of data warehouses, semantic layers, and governance tools will form the foundation for AI Agent workflows, evolving beyond traditional reporting systems [10][12] - The valuation of AI platforms will increasingly depend on their ability to define and manage facts, rather than just their user interfaces [14][15] - Companies that can create exceptional AI Agent experiences based on reliable data sources will have a competitive advantage in the evolving landscape [15]

想象一下：你站在1985年一家繁忙的零售店里。每当顾客购买商品，收银机都会立即记录交易信息——商品、价格、时间 戳。这便是店铺的脉搏，是运营的脉动。现在想象一下，你试图回答以下问题："上个季度哪些商品最畅销？"或者"我们所 有门店的营收趋势如何？"突然间，这套每秒处理数千笔交易的系统却难以给出答案。这就像让一个短跑运动员去跑马拉松 ——同一个运动员，却要参加完全不同的比赛。 记录当下发生的事情与理解其意义之间的根本张力，推动了数据系统五十年来的创新。今天，我们将追溯这一发展历程，从 最早的事务型数据库到如今能够用自然语言回答问题的AI驱动型分析平台。理解这一演变过程不仅仅是了解历史，更是我们 今天做出更佳架构决策的路线图。 数据的两个世界：OLTP 与 OLAP 在深入探讨时间线之前，让我们先确定塑造之后一切的核心区别。 在线交易处理 (OLTP)系统旨在处理企业的日常运营。您可以将其想象成收银机——它们需要快速、准确，并且始终可用， 以记录每一笔交易。当您在线订购商品、更新个人资料或在账户之间转账时，您就是在与 OLTP 系统交互。 另一方面，OLAP（联机分析处理）系统是为分析和报告而设计的。它们是会计人 ...

Core Viewpoint - UBS's participation in the Databricks investor day indicates a strong ongoing investment in the "data layer," which may benefit both Databricks and Snowflake despite their competition [1] Databricks Disclosure - Databricks expects a revenue run rate of $3.7 billion for the second half of the year, representing a year-over-year growth of approximately 50% [2] - Databricks anticipates its data warehouse revenue run rate will exceed $1 billion this year, which aligns with expectations and does not raise concerns about Snowflake's market share loss [2] - Databricks' "AI suite" has an annual recurring revenue (ARR) of $300 million, surpassing Snowflake [2] - The CEO of Databricks has adopted a more neutral stance towards Snowflake compared to the past [2] - Demand for Postgres databases is described as "very hot," which may not bode well for MongoDB [2] - Most enterprises are still in the early stages of deploying AI agents, with much of the activity being speculative [2] - Demand in the Europe, Middle East, and Africa (EMEA) markets is reported to be weak [2] Customer/Partner Feedback - Feedback from clients regarding Databricks is overwhelmingly positive, particularly concerning product functionality, pricing, and innovation speed [2] - Feedback on Snowflake is unexpectedly constructive, with clients noting that the development pace of Snowflake and Databricks appears similar, a sentiment not expressed two years ago [3] - Enterprises are attempting to organize data for AI applications, supported by feedback from interviews [3] - Adoption of data lake or iceberg technology is reported to be more positive than anticipated [3] Valuation - UBS maintains that if Snowflake's growth rate trends towards 30% and the data investment cycle remains prolonged, a multiple of 13x/51x CY26E revenue/free cash flow (FCF) does not seem unreasonable [3] - The target price for Snowflake remains at $265, based on a multiple of 17x/66x CY26E, which is considered a reasonable premium relative to high-growth peers [3]