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Multi-dataset Topic best practices for Amazon Quick Chat | Amazon Web Services
This post is for data architects, business intelligence (BI) engineers, and analytics engineers building or optimizing Quick Sight Topics for natural-language Chat-based exploration.
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Build a unified semantic layer across datasets with multi-dataset Topics in Amazon Quick | Amazon Web Services
In this post, we walk through how multi-dataset Topics work, explain how the chat agent uses defined relationships to generate cross-dataset queries, and demonstrate an end-to-end…
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Multi-dataset Topic best practices for Amazon Quick Chat | Amazon Web Services
This post is for data architects, business intelligence (BI) engineers, and analytics engineers building or optimizing Quick Sight Topics for natural-language Chat-based…
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Data modeling best practices for Amazon Quick Sight multi-dataset relationships | Amazon Web Services
Today, we are excited to announce Multi-Dataset Relationships in Amazon Quick Sight. This new capability lets you define logical relationships between Quick Sight datasets and…
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Enrich your datasets with business context: Migrating from legacy Topics to semantic datasets in Amazon Quick | Amazon Web Services
In this post, we walk through what Dataset Enrichment is, how it differs from legacy Topics, and provide three migration scenarios with step-by-step guidance so you can move your…