If you’ve been managing Amazon Quick legacy Topics alongside your datasets, you know the challenge: two assets that must stay perfectly synchronized, each with its own permissions, lineage, and versioning. Column synonyms drift. Calculated fields diverge. A rename in the dataset breaks the Legacy Topic silently. You can now use Amazon Quick to embed that business context directly into the dataset itself through Dataset Enrichment in the new data prep experience. Column descriptions, synonyms, calculated fields, custom instructions, and business rules all live alongside the data. Dataset Enrichment bakes business context directly into the dataset. Everything (permissions, semantics, AI context) travels with the data and is automatically inherited by anything built on top of it. One asset, one source of truth, one place to govern.

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 business context into the dataset layer with confidence.

Topic is now the multi-dataset semantic and reasoning layer, the construct where multiple datasets are composed, relationships are defined, business metrics are authored, and business terminology is mapped. Rather than introducing a net-new construct, we are re-purposing Topic to fulfill this role more completely. Moving dataset-intrinsic semantics down to where they belong, and elevating Topic to own the cross-dataset relationships, metrics, and business terminology that it was always meant to carry. This isn’t a cosmetic change. It establishes a clean, forward-looking architecture that supports both deterministic BI workflows and flexible AI-driven analytics from a shared semantic foundation. It also sets up the framework for catalog integration.