AI-powered BI with Snowflake and Amazon Quick
Article summary
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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…
1Key Takeaways
- 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….
2AIWedia Score
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3Why it matters
Cloud AI updates influence enterprise budgets, latency, and which stack teams standardize on. AWS ML Blog reports that in this post, you will learn how to build an end-to-end integration between Snowflake semantic views and Amazon Quick.
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