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]

传统厂商长期占据主导地位，却也因云原生分布式数据库的冲击而有所动摇。当AI风暴再次搅动这个 市场，数据库厂商纷纷调整数据战略，试图更加贴近企业客户使用AI的实际场景。新旧势力间的角 逐，实际也让真实的客户加快享受到新时代红利。 图片来源@pixabay 生成式AI技术变革，正驱使数据库厂商展开激烈竞争。 在Databricks的案例库中流传着这样一个故事。来自中国的全球消费电子品牌安克创新，其数据团队曾 遇到过一个棘手问题：受原有数仓的制约，无法对企业内部多个系统和应用数据进行统一治理，这导致 团队将大量时间用于数据治理及相关的Devops落地，几乎没有时间挖掘更高价值的数据任务，比如用 于支持生成式AI的创新。 安克创新用上了云湖仓，而针对这一产品的选型中，其团队放弃了Snowflake，而选择Databricks，其关 键一点在于技术层面，前者并不符合团队对操作便捷性和基于同一平台实现数据、分析、AI创新的需 求。而基于Databricks的云湖仓产品Delta Lake实现200TB数据的统一数据底座后，安克创新又陆续用上 了Databricks的其他产品：通过Unity Catalog实现表格式数据访 ...