Amazon Quick supports multi-dataset analytical capability

Amazon Quick has announced general availability of multi-dataset topics, enabling users to model relationships across multiple datasets within a single topic and utilize that model to build dashboards and answer questions in natural language. Previously, answering a question in a Quick Sight visual that spanned datasets required pre-joining the data into a single dataset-adding manual JOIN logic during data preparation, consuming extra SPICE capacity, and requiring users to rebuild multiple datasets for different use cases or when the model changed. With multi-dataset topics, a topic becomes a reusable relational data model: users add multiple datasets, define the relationships once, and Quick performs the joins at runtime. This capability applies to both dashboard building and natural-language Q&A. For dashboard building, the topic acts as the data model, allowing a single visual to draw fields from multiple datasets with Quick generating the underlying join automatically-users author the semantic model once and reuse it across every visual in the dashboard. For natural-language analytics, users can direct a chat agent to a topic and ask questions; the agent reads the relationships defined in the topic and performs runtime joins across datasets to answer, with no need to pre-join tables or prepare data first. Since both experiences query the same topic, one governed semantic model serves as the single source of truth for both people and agents. Multi-dataset topics reuse existing dataset permissions and support row-level and column-level security (RLS/CLS), ensuring established governance carries through every cross-dataset visual and every answer. Multi-dataset topics are now generally available in all AWS Regions where Amazon Quick is offered.