mirna's tweaks to nick and roxi's updates

website/docs/docs/dbt-cloud-apis/sl-api-overview.md

@@ -26,7 +26,7 @@ You can use the dbt Semantic Layer for a variety of tools and applications of da
 * Data discovery and cataloging
 * Machine learning and data science
 
-The dbt Semantic Layer APIs are available for accounts on [Team or Enterprise plans](https://www.getdbt.com/pricing/), allowing them to query metrics and build integrations. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally. 
+During [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta), the dbt Semantic Layer is accessible to all dbt Cloud Team and Enterprise multi-tenant plans [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America. It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools. 
 
 <div className="grid--3-col">
 

website/docs/docs/dbt-cloud-apis/sl-graphql.md

@@ -19,16 +19,17 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 With GraphQL, users can request specific data using a single query, reducing the need for many server round trips. This improves performance and minimizes network overhead.
 
 GraphQL has several advantages, such as self-documenting, having a strong typing system, supporting versioning and evolution, enabling rapid development, and having a robust ecosystem. These features make GraphQL a powerful choice for APIs that prioritize flexibility, performance, and developer productivity.
-
-dbt Partners can use the Semantic Layer GraphQL API to build and integration with the dbt Semantic Layer.
-
 ## dbt Semantic Layer GraphQL API
 
 The dbt Semantic Layer GraphQL API allows you to explore and query metrics and dimensions. Due to it's self-documenting nature, you can explore the calls conveniently through the [schema explorer](https://cloud.getdbt.com/semantic-layer/api/graphql). 
 
+dbt Partners can use the Semantic Layer GraphQL API to build and integration with the dbt Semantic Layer.
+
 ## Using the GraphQL API
 
-If you are a dbt user or partner with access to dbt Cloud and the Semantic Layer, you can setup and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right GQL connection parameters described in this document. (PROVIDE link to set up?). 
+If you're a dbt user or partner with access to dbt Cloud and the[dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right GQL connection parameters described in this document. 
+
+Refer to [Get started with the dbt Semantic Layer](docs/use-dbt-semantic-layer/quickstart-sl) for more info.
 
 ### Authentication 
 
@@ -38,7 +39,7 @@ Authentication uses a dbt Cloud Service token passed through a header as follows
 {"Authorization": "Bearer <SERVICE TOKEN>"}
 ```
 
-Each GQL request also comes with a dbt Cloud Environment Id. Our API will use the combination of the Service Token in the header and Environment Id to authenticate.
+Each GQL request also comes with a dbt Cloud environmentId. The API uses both the service token in the header and environmentId for authentication.
 
 
 ### Metric metadata calls

website/docs/docs/dbt-cloud-apis/sl-jdbc.md

@@ -29,7 +29,9 @@ Partners of dbt Labs can use the JDBC API to build integrations in their tools w
 
 ## Using the JDBC API
 
-If you are a dbt user or partner with access to dbt Cloud and the Semantic Layer, you can setup and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document. (PROVIDE link to set up?). 
+If you are a dbt user or partner with access to dbt Cloud and the[dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document.
+
+Refer to [Get started with the dbt Semantic Layer](docs/use-dbt-semantic-layer/quickstart-sl) for more info.
 
 ## Connection parameters
 

website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md

@@ -7,11 +7,11 @@ date: 2023-07-31
 sidebar_position: 9
 ---
 
-We are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), now available in [public beta](#public-beta). It aims to bring the best of modeling and semantics to downstream applications by introducing:
+dbt Labs are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), now available in [public beta](#public-beta). It aims to bring the best of modeling and semantics to downstream applications by introducing:
 
-- [MetricFlow](/docs/build/about-metricflow), a framework for constructing performant and legible SQL from an all new set of semantic constructs which include semantic models, entities, and metrics.
-- New Semantic Layer Infrastructure that enables support for more data platforms (Snowflake, Databricks, BigQuery, Redshift and soon more) and improved performance.
-- New and Improved [developer workflows](/guides/migration/sl-migration), governance, and collaboration features.
+- [MetricFlow](/docs/build/about-metricflow) is a framework for constructing performant and legible SQL from an all new set of semantic constructs which include semantic models, entities, and metrics.
+- New Semantic Layer infrastructure that enables support for more data platforms (Snowflake, Databricks, BigQuery, Redshift, and soon more), along with improved performance.
+- New and improved [developer workflows](/guides/migration/sl-migration), governance, and collaboration features.
 - New [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to query metrics and build integrations.
 
 With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
@@ -24,15 +24,15 @@ What sets the dbt Semantic Layer apart is its ability to centralize logic for ma
 
 We are excited to present several important capabilities with the enhanced dbt Semantic Layer:
 
-- **Consistent organization**: Provides a consistent view of data, ensuring that metrics and definitions match across the organization and teh breadth of interfaces where data is consumed. This fosters trust in data and drives better decision-making by eliminating inconsistencies and errors that come up when individual users define metrics independently.
+- **Consistent organization**: Provides a consistent view of data, ensuring that metrics and definitions match across the organization and the breadth of interfaces where data is consumed. This fosters trust in data and drives better decision-making by eliminating inconsistencies and errors that come up when individual users define metrics independently.
 
 - **Improved governance**: The dbt Semantic Layer ensures proper governance and auditing of data changes, providing an auditable record of modifications and clear ownership. This saves time by making it clear who can create and manage new metrics, ensuring accountability and data integrity.
 
 - **Reduce costs**: The dbt Semantic Layer simplifies complex tasks, such as bridging entities across a semantic graph. Often users duplicate slices and dice of data and make them available in a data platform, making it difficult to manage and causing high computation. The dbt Semantic Layer minimizes duplication of work and reduces computational costs - allowing users to focus on analyzing data rather than navigating intricate technical processes or duplicating work.
 
 - **Enhanced efficiency**: With the dbt Semantic Layer, data teams can create and update metrics using a new set of validations that make defining and iterating on metrics efficient. The streamlined development workflows makes it simpler for a data team to serve large organizations with broad data needs.
 
-- **Accessible data**: Defining common metrics and dimensions and making them joinable, makes access simpler for users with less expertise in the specifics of a companies data modeling work. This creates opportunities to leverage data insights, fostering collaboration and driving innovation in a more inclusive data environment.
+- **Accessible data**: Defining common metrics and dimensions and making them joinable, makes access simpler for users with less expertise in the specifics of a company's data modeling work. This creates opportunities to leverage data insights, fostering collaboration and driving innovation in a more inclusive data environment.
 
 By bringing these enhancements to the dbt Semantic Layer, we enable organizations of all sizes and industries to leverage the power of semantics in their data workflows.
 
@@ -42,11 +42,11 @@ The dbt Semantic Layer is currently available as a public beta, which means:
 
 - **Who** — To experience the new dbt Semantic Layer, you must be on a dbt Cloud [Team and Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plan, [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America and on dbt v1.6 and higher. Look out for announcements on removing the location requirement soon.
 
-  - Developer plans or dbt Core users can use MetricFlow to define and test metrics using the dbt-metricflow CLI only.
+  - Developer plans or dbt Core users can use MetricFlow to define and test metrics using the dbt MetricFlow CLI only.
 
-- **What** — Public beta provides early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability later this year. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer in October 2023. For now, there will be no billing for usage. If you have any questions on pricing please reach out to your account rep.
+- **What** — Public beta provides early access to new features. The dbt Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability later this year. We may also introduce new functionality that isn't backwards compatible. We provide support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer in October 2023. For now, there will be no billing for usage. If you have any questions on pricing please reach out to your account representative.
 
-- **When** — Public beta starts on July 31 and will end once the dbt Semantic Layer is available for GA in October 2023.
+- **When** — Public beta starts on July 31, 2023 and will end once the dbt Semantic Layer is available for GA in October 2023.
 
 - **Where** — You can experience the dbt Semantic Layer in dbt Cloud. Public beta is enabled at the account level so you don’t need to worry about enabling it per user.
 

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

@@ -22,7 +22,7 @@ To read more about why you need a universal Semantic Layer, read this [blog post
 
 ## Explore the dbt Semantic Layer
 
-During [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta), the dbt Semantic Layer is accessible to all dbt Cloud Team and Enterprise multi-tenant plans [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America. It's available on dbt v1.6 or newer. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.
+During [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta), the dbt Semantic Layer is accessible to all dbt Cloud Team and Enterprise multi-tenant plans [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America. It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.
 
 <div className="grid--3-col">
 

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

@@ -150,7 +150,7 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 ## Connect and query APIs
 
-add content here
+This step requires you to connect to the [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview). Once you've connected to an API, you should then set up and query metrics in your downstream tool of choice. Refer to [dbt Semantic Layer Apis](/docs/dbt-cloud-apis/sl-api-overview) and [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
 ## FAQs
 

website/snippets/_new-sl-setup.md

@@ -11,9 +11,11 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 4. Enter the credentials you want the Semantic Layer to use specific to your data platform. 
     * Note: We recommend using a less privileged set of credentials because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
 5. Select the deployment environment you want for the Semantic Layer
-6. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
-7. Save & copy your environment ID, service token, and host for inputting into a downstream tool
-8. Great job, you've configured the Semantic Layer 🎉! 
+6. You should see connection information that allows you to connect to downstream tools. 
+    * If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.
+7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
+8. Save & copy your environment ID, service token, and host for inputting into a downstream tool
+9. Great job, you've configured the Semantic Layer 🎉! 
 
 
 

website/snippets/_v2-sl-prerequisites.md

@@ -1,15 +1,15 @@
 
 <VersionBlock firstVersion="1.6">
 
-- Have a [multi-tenant dbt Cloud](/docs/deploy/regions) instance, hosted in North America
+- Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America
 - Have both your production and development environments running dbt version 1.6 or higher 
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
   * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
-- Install the [MetricFlow command line (CLI)](https://github.com/dbt-labs/metricflow) package
+- Install the [MetricFlow CLI](/docs/build/metricflow-cli)
   * Note &mdash; After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) in the integrated tool to import metric definitions 
-  * Developer accounts will be able to query manually via the CLI using SQL. To dynamically query metrics using external tools, you'll need access to the Semantic Layer APIs.<br />
+  * Developer accounts will be able to query manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you'll need access to the Semantic Layer APIs.<br />
 
 
 </VersionBlock>