Merge branch 'current' into mwong-edits

website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md

@@ -7,7 +7,7 @@ sidebar_label: "dbt Semantic Layer"
 
 <Snippet src="sl-public-preview-banner" />
 
-The dbt Semantic Layer allows data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions.
+The dbt Semantic Layer allows data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
 
 The result? You have less duplicative coding for data teams and more consistency for data consumers. 
 
@@ -23,7 +23,7 @@ The dbt Semantic Layer has four main parts:
 
 ### What makes the dbt Semantic Layer different?
 
-The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications.
+The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. You can also use the dbt Semantic Layer to query models and use macros.
 
 
 ## Prerequisites
@@ -77,7 +77,7 @@ The dbt Semantic Layer product architecture includes four primary components:
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
-| **[dbt metrics](/docs/build/metrics)** | Allows you to define metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source, Core |
+| **[dbt project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source, Core |
 | **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
 | **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 | **[Metadata API](/docs/dbt-cloud-apis/metadata-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, metrics). The Metadata API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise |