Enrich your datasets with business context: Migrating from legacy Topics to semantic datasets in Amazon Quick

This article explains how to migrate business context from legacy Topics to Dataset Enrichment in Amazon QuickSight, consolidating semantic metadata into a single dataset asset.

• Dataset Enrichment embeds column descriptions, synonyms, calculated fields, and business rules directly into dataset metadata
• Legacy Topics are repurposed as a multi-dataset semantic layer for cross-dataset relationships and business metrics
• Three migration scenarios covered: legacy datasets with no Topics, legacy Topics with legacy datasets, and legacy Topics with new data prep datasets
• Scenario 3 enables direct in-place migration via Python script using QuickSight APIs to extract and transform metadata
• Column-level metadata includes descriptions and synonyms; dataset-level metadata includes custom instructions for business logic and rules
• Validation best practices include side-by-side testing, edge case verification, and user acceptance testing before retiring legacy Topics

Dataset Enrichment establishes a clean architecture where business metadata travels with data, simplifying governance and enabling AI-ready self-describing datasets.