用Snowflake和Amazon Quick构建AI驱动的BI工作流
AI-powered BI with Snowflake and Amazon Quick
这篇教程手把手教你搭一个能自然语言查数据的BI系统，从数据加载到仪表板生成全都有，还能用Cortex Analyst问问题。
本文教程演示如何将Snowflake语义视图与Amazon Quick集成，实现自然语言BI查询。通过加载S3中的电影评论数据到Snowflake，使用SQL定义语义视图，再通过Cortex Analyst进行自然语言查询。
最后自动化生成Amazon Quick数据集和仪表板，使得BI团队能用自然语言访问受治理的数据层。整个过程可手动或通过脚本完成，确保响应反映一致业务逻辑。
AI-powered BI with Snowflake and Amazon Quick
In this post, you will learn how to build an end-to-end integration between Snowflake semantic views and Amazon Quick. The sample data is user review data for a media company. You start by loading movie review data from Amazon Simple Storage Service (Amazon S3) into Snowflake, define a semantic view in SQL to add business meaning, explore it with natural-language queries through Cortex Analyst, and then generate an Amazon Quick dataset and dashboard. The dataset can be created manually or with a provided automation script. By the end, your BI team or AI team can ask natural-language questions against a governed data layer and trust that every response reflects the same business logic.
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Snowflake, Amazon Quick, Cortex Analyst, 自然语言查询.
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