Multi-dataset Topic best practices for Amazon Quick Chat | Amazon Web Services
Note: The topics referenced throughout this document refer to the new Topics experience (not legacy Topics). For details on the differences, see Build a unified semantic layer across datasets with multi-dataset Topics in Amazon Quick.
Most real-world business questions span multiple tables. A retailer who wants to understand net revenue by product category must draw from a sales fact table, a returns fact table, and a product dimension. Each of these lives in a separate dataset. Until recently, bridging those datasets required a data engineer to pre-join them and deliver a single dataset to Amazon Quick Sight before any analyst could ask a question.
Amazon Quick Sight’s Multi-Dataset Topics change that equation by letting analytics teams bring multiple datasets into a single Topic in one of two ways. You can define explicit relationship keys (covered in the companion post, Data modeling best practices for Amazon Quick Sight multi-dataset...
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