add sl and env img (#6279)

website/docs/docs/dbt-cloud-environments.md

@@ -15,8 +15,11 @@ Critically, in order to execute dbt, environments define three variables:
 
 Each dbt Cloud project can have only one [development environment](#create-a-development-environment), but there is no limit to the number of [deployment environments](/docs/deploy/deploy-environments), providing you the flexibility and customization to tailor the execution of scheduled jobs. 
 
-Use environments to customize settings for different stages of your project and streamline the execution process by using software engineering principles. This page will detail the different types of environments and how to intuitively configure your development environment in dbt Cloud. 
+Use environments to customize settings for different stages of your project and streamline the execution process by using software engineering principles. 
 
+<Lightbox src="/img/dbt-env.png" width="90%" title="dbt Cloud environment hierarchy showing projects, environments, connections, and orchestration jobs." />
+
+The following sections detail the different types of environments and how to intuitively configure your development environment in dbt Cloud. 
 
 import CloudEnvInfo from '/snippets/_cloud-environments-info.md';
 

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

@@ -13,6 +13,8 @@ The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), s
 
 Moving metric definitions out of the BI layer and into the modeling layer allows data teams to 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. To ensure secure access control, the dbt Semantic Layer implements robust [access permissions](/docs/use-dbt-semantic-layer/setup-sl#set-up-dbt-semantic-layer) mechanisms.
 
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-concept.png" width="80%" title="This diagram shows how the dbt Semantic Layer works with your data stack." />
+
 Refer to the [dbt Semantic Layer FAQs](/docs/use-dbt-semantic-layer/sl-faqs) or [Why we need a universal semantic layer](https://www.getdbt.com/blog/universal-semantic-layer/)  blog post to learn more.
 
 ## Get started with the dbt Semantic Layer

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

@@ -28,6 +28,8 @@ The primary value of the dbt Semantic Layer is to centralize and bring consisten
 - **Simplify your code** by not duplicating metric logic and allowing MetricFlow to perform complex calculations for you.
 - **Empower stakeholders** with rich context and flexible, yet governed experiences.
 
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-concept.png" width="90%" title="This diagram shows how the dbt Semantic Layer works with your data stack." />
+
 </Expandable>
 
 <Expandable alt_header="What's the main difference between the dbt Semantic Layer and dbt Metrics?">
@@ -110,6 +112,9 @@ You can use tables and dbt models to calculate metrics as an option, but it's a
 If you create a table with a metric, you’ll need to create numerous other tables derived from that table to show the desired metric cut by the desired dimension or time grain. Mature data models have thousands of dimensions, so you can see how this will quickly result in unnecessary duplication, maintenance, and costs. It's also incredibly hard to predict all the slices of data that a user is going to need ahead of time.
 
 With the dbt Semantic Layer, you don’t need to pre-join or build any tables; rather, you can simply add a few lines of code to your semantic model, and that data will only be computed upon request.
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-concept.png" width="90%" title="This diagram shows how the dbt Semantic Layer works with your data stack." />
+
 </Expandable>
 
 <Expandable alt_header="Do I materialize anything when I define a semantic model?">