Since around 2025, a quiet change has been spreading through the field of data analysis. When a member of the marketing team types, "What percentage of new customers from last month signed enterprise ...

Enterprise AI deployments face significant challenges at the database layer rather than the model layer. The data stack was not designed for AI agents, resulting in rising costs and inefficiencies. A ...

MSc Business Analytics portfolio: applied projects in SQL analytics, experimentation, machine learning, optimisation, statistics, and AI data products (RAG, semantic search, computer vision). Pytho ...

Couchbase AI Data Plane combines persistent agent memory, vector search and an enterprise MCP server that runs on-device when ...

AI 正在改变企业的运营方式。AI 智能体能够带来卓越的客户体验、实现工作流程自动化、降低成本, 并以前所未有的速度释放新的商业机遇。但 AI 同时也在从根本上改变安全态势。 AI 系统正在为敏感企业数据开辟新的访问路径。AI ...

Why local AI’s the way forward, and the best way period With hardware prices spiraling, AI vendors ramping up token costs, and models becoming drastically slimmer and more economical, running AI ...

From RAG to ontology: Databricks bets on context as the key to trusted AI agents Genie Ontology aims to unify business definitions across systems, but analysts say data quality and governance will ...

AI时代苟日新，日日新，又日新，数据库也是如此。 主流数据库的发展经历了几次重要演进：从最早的OLTP数据库，到OLAP从其中分离出来成为数据仓库，再到大数据系统。长期以来，数据库架构主要围绕人类应用、确定性交易和结构化数据分析设计。 今天，新的变化正在发生。 AI ...

在人工智能技术迅猛发展的当下，数据库领域正经历一场深刻变革。传统数据库的设计理念主要围绕人类应用、确定性交易和结构化数据分析展开，但随着AI Agent的兴起，这一模式正面临前所未有的挑战。这些智能体不再局限于简单的数据查询，而是能够自主调用工具、生成代码、执行任务，甚至参与业务流程，对数据库提出了全新的需求。