In Part 1 of this series, we introduced Amazon Quick Sight Multi-Dataset Relationships and covered the foundational concepts of dimensional modeling, best practices for designing clean data models, and a decision framework for when to use runtime joins versus pre-joined datasets. If you haven’t read Part 1 yet, we recommend starting there.

In this post, we shift from concepts to patterns. For each schema, you’ll find a table structure, use cases, implementation steps, and sample SQL queries. We also cover workarounds for advanced scenarios that require extra modeling steps, and close with a summary of current limitations.

Note: All Multi-Dataset relationships in the current release use inner join. Only rows with matching keys in both datasets appear in query results. Design your data model accordingly.

Supported patterns

The following seven scenarios are natively supported by Quick Sight Multi-Dataset Relationships. Each scenario maps to a common data modeling pattern, with concrete implementation guidance and sample SQL.