一水 发自 凹非寺 量子位 | 公众号 QbitAI AI时代，真是啥都要快。 三行代码构建一个AI应用 ，现在就这样被 蚂蚁OceanBase 游刃有余地实现了。 熟悉数据库的朋友都知道，OceanBase是蚂蚁集团于2010年发布的一款全自研的 国产企业级分布式关系数据库 。 历经15年迭代升级，如今它不仅在权威测评中稳居国产数据库第一梯队，其稳定性更是经过了"双十一"等高并发场景的持续验证。 悄悄补充一句，OceanBase还在现场秀出了最新成绩单——全球客户数已突破4000家，连续五年年均增速超100%，服务覆盖16个国家和地 区、60多个地域、240多个可用区。 而且截至今年5月，经过四年开源实践， OceanBase已形成拥有超过25000名开发者的活跃社区，累计下载量突破百万 。 但随着AI时代的到来，OceanBase也迎来了新的考验： AI时代需要怎样的数据库？OceanBase如何继续保持领先？ 一切答案，都藏在刚刚落幕的 2025 OceanBase年度发布会 上了—— 这一次，他们不是用语言，而是用实打实的 产品 给出最新思考。 面向AI时代的原生混合搜索数据库 具体而言，OceanB ...

Core Insights - Data has become one of the most valuable assets for organizations, playing a crucial role in strategic decision-making, operational optimization, and gaining competitive advantages [1] - Data engineering is a key discipline that manages the entire process from data collection to transformation, storage, and access [1] - Organizations are shifting towards architectures that can respond to various data needs, with data management strategies like data warehouses, data lakes, data lakehouses, and data meshes playing significant roles [1] Group 1: Data Management Strategies - Data warehouses focus on structured data and are optimized for reporting and analysis, allowing for easy data retrieval and high-performance reporting [12][15] - Data lakes provide a flexible structure for storing structured, semi-structured, and unstructured data, making them suitable for big data projects and advanced analytics [21][24] - Data lakehouses combine the flexibility of data lakes with the structured data management capabilities of data warehouses, allowing for efficient analysis of various data types [27][30] Group 2: Data Architecture Design - A solid data architecture design is critical for the success of data warehouse projects, defining how data is processed, integrated, stored, and accessed [9] - The choice of data architecture design method should align with project goals, data types, and expected use cases, as each method has its advantages and challenges [10][43] - The Medallion architecture is a modern data warehouse design that organizes data processing into three layers: bronze (raw data), silver (cleaned data), and gold (business-ready data) [57][65] Group 3: Implementation Considerations - Effective demand analysis is essential for avoiding resource and time wastage, ensuring that the specific needs of the organization are clearly understood before starting a data architecture project [3][8] - The integration of data from various sources, such as ERP and CRM systems, requires careful planning and robust data control throughout the ETL process [4][6] - Documentation of the data model is crucial for ensuring that both technical teams and business users can easily adapt to the system, impacting the project's sustainability [5][6]