add exports content and rn

website/dbt-versions.js

@@ -174,10 +174,6 @@ exports.versionedPages = [
     "page": "reference/resource-configs/grants",
     "firstVersion": "1.2",
   },
-  {
-    "page": "docs/build/saved-queries",
-    "firstVersion": "1.7",
-  },
   {
     "page": "reference/resource-configs/on_configuration_change",
     "firstVersion": "1.6",

website/docs/docs/build/about-metricflow.md

@@ -17,7 +17,7 @@ Before you start, consider the following guidelines:
 - Define metrics in YAML and query them using these [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
 - You must be on [dbt version](/docs/dbt-versions/upgrade-core-in-cloud) 1.6 or higher to use MetricFlow. 
 - Use MetricFlow with Snowflake, BigQuery, Databricks, Postgres (dbt Core only), or Redshift. 
-- Discover insights and query your metrics using the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and its diverse range of [available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).  You must have a dbt Cloud account on the [Team or Enterprise plan](https://www.getdbt.com/pricing/). 
+- Discover insights and query your metrics using the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and its diverse range of [available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations). 
 
 ## MetricFlow
 

website/docs/docs/build/build-metrics-intro.md

@@ -22,14 +22,14 @@ MetricFlow allows you to:
 <div className="grid--3-col">
 
  <Card
-    title="Get started with the dbt Semantic Layer"
+    title="Get started with the dbt Semantic Layer and MetricFlow"
     body="Use this guide to build and define metrics with MetricFlow, set up the dbt Semantic Layer, and query them using downstream tools."
     link="/docs/build/sl-getting-started"
     icon="dbt-bit"/>
 
 <Card
     title="About MetricFlow"
-    body="Understand MetricFlow's core concepts, key principles, and how to use this powerful tool."
+    body="Understand MetricFlow's core concepts, how to use joins, how to save commonly used queries, and what commands are available."
     link="/docs/build/about-metricflow"
     icon="dbt-bit"/>
 

website/docs/docs/build/saved-queries.md

@@ -8,15 +8,21 @@ tags: [Metrics, Semantic Layer]
 
 Saved queries are a way to save commonly used queries in MetricFlow. You can group metrics, dimensions, and filters that are logically related into a saved query. 
 
-To define a saved query, refer to the following specification:
+### Exports and saved queries comparison
 
- Parameter | Description | Type |
-| --------- | ----------- | ---- |
-| `name` | The name of the metric. | Required |
-| `description` | The description of the metric. | Optional |
-| `query_params` | The query parameters for the saved query: `metrics`, `group_by`, and `where`. | Required |
+Saved queries are distinct from [exports](/docs/use-dbt-semantic-layer/exports), which schedule and execute saved queries using [dbt Cloud's job scheduler](/docs/deploy/job-scheduler). The following table compares the features and usage of exports and saved queries:
 
-The following is an example of a saved query:
+| Feature |  Exports | <div style={{width:'250px, text-align: center'}}>Saved queries</div>  |
+| ----------- | ----------- | ---------------- |
+| **Availability**    | Available on dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) plans on dbt versions 1.7 or newer.| Available in both dbt Core and dbt Cloud.     |
+| **Purpose**         | To materialize saved queries in your data platform and expose metrics and dimensions as a view or table. | To define and manage common Semantic Layer queries in YAML, which includes metrics and dimensions.   |
+| **Usage**           | Automatically runs saved queries and materializes them within your data platform. Exports count towards [queried metrics](/docs/cloud/billing#what-counts-as-a-queried-metric) usage. <br /><br />**Example**: Creating a weekly aggregated table for active user metrics, automatically updated and stored in the data platform.  | Used for organizing and reusing common MetricFlow queries within dbt projects.<br /><br /><br />**Example**: Group related metrics together for better organization, and include commonly uses dimensions and filters. | For materializing query results in the data platform. |
+| **Integration**     | Must have the dbt Semantic Layer configured in your dbt project.<br /><br />Tightly integrated with the [MetricFlow Server](/docs/use-dbt-semantic-layer/sl-architecture#components) and dbt Cloud's job scheduler. | Integrated into the dbt <Term id="dag" /> and managed alongside other dbt nodes. |
+| **Configuration**   | Configured within dbt Cloud environment and job scheduler settings. | Defined in YAML format within dbt project files.   |
+
+All metrics in a saved query need to use the same dimensions in the `group_by` or `where` clauses. The following is an example of a saved query:
+
+<File name='semantic_model.yml'>
 
 ```yaml
 saved_queries:
@@ -32,5 +38,31 @@ saved_queries:
       where:
         - "{{ Dimension('listing__capacity_latest') }} > 3"
 ```
+</File>
+
+## Parameters
+
+To define a saved query, refer to the following parameters:
+
+| Parameter | Type    | Required | Description    |
+|-------|---------|----------|----------------|
+| `name`       | String    | Required     | Name of the saved query object.          |
+| `description`     | String      | Required     | A description of the saved query.     |
+| `query_params`       | Structure   | Required     | Contains the query parameters. |
+| `query_params::metrics`   | List or String   | Optional    | A list of the metrics to be used in the query as specified in the command line interface. |
+| `query_params::group_bys`    | List or String          | Optional    | A list of the Entities and Dimensions to be used in the query, which include the `Dimension` or `TimeDimension`. |
+| `query_params::where`        | LList or String  or String | Optional  | A list of string which may include the `Dimension` or `TimeDimension` objects. |
+| `exports`     | List or Structure | Optional    | A list of exports to be specified with the exports structure.     |
+| `exports::name`       | String               | Required     | Name of the export object.      |
+| `exports::config`     | List or Structure     | Required     | A config section for any parameters specifying the export.  |
+| `exports::config::export_as` | String    | Required     | The type of export to run. Options include table or view currently and cache in the near future.   |
+| `exports::config`   | String   | Optional    | The schema used for creating the table or view. This option cannot be used for caching.   |
+| `exports::config`  | String     | Optional    | The table alias to use to write the table or view.  This option cannot be used for caching.  |
 
 All metrics in a saved query need to use the same dimensions in the `group_by` or `where` clauses.
+
+
+## Related docs
+
+- [Exports](/docs/use-dbt-semantic-layer/exports)
+- [Set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl)

website/docs/docs/dbt-versions/release-notes/72-Feb-2024/exports-rn.md

@@ -0,0 +1,16 @@
+---
+title: "New: Use exports to materialize saved queries"
+description: "February 2024: Use the exports feature to materialize and schedule saved queries with dbt Cloud, and integrate with additional tools."
+sidebar_label: "New: Use exports to materialize saved queries"
+sidebar_position: 09
+tags: [Feb-2024]
+date: 2024-02-07
+---
+
+You can now use the [exports](/docs/use-dbt-semantic-layer/exports) feature with [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), allowing you to query reliable metrics and fast data reporting. Exports enhance the saved queries feature, allowing you to materialize commonly used queries directly within your data platform using dbt Cloud's job scheduler.
+
+By exposing tables of metrics and dimensions, exports enable you to integrate with additional tools that don't natively connect with the dbt Semantic Layer, such as PowerBI.
+
+Exports are available for dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) plans on dbt versions 1.7 or newer.
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/deploy_exports.jpg" width="90%" title="Add an environment variable to run exports in your production run." />

website/docs/docs/deploy/deploy-jobs.md

@@ -13,7 +13,7 @@ You can use deploy jobs to build production data assets. Deploy jobs make it eas
 - Job run details, including run timing, [model timing data](#model-timing), and [artifacts](/docs/deploy/artifacts)
 - Detailed run steps with logs and their run step statuses
 
-You can create a deploy job and configure it to run on [scheduled days and times](#schedule-days) or enter a [custom cron schedule](#custom-cron-schedules). 
+You can create a deploy job and configure it to run on [scheduled days and times](#schedule-days) or enter a [custom cron schedule](#custom-cron-schedules).
 
 
 ## Prerequisites

website/docs/docs/deploy/deployment-overview.md

@@ -16,6 +16,7 @@ dbt Cloud offers the easiest and most reliable way to run your dbt project in pr
 - Identify the root cause of failures in deployment environments
 - Maintain high-quality code and data in production
 - Gain visibility into the health of deployment jobs, models, and tests
+- Uses [exports](/docs/use-dbt-semantic-layer/exports) to materialize [saved queries](/docs/build/saved-queries) in your data platform for reliable and fast metric reporting
 
 Before continuing, make sure you understand dbt's approach to [deployment environments](/docs/deploy/deploy-environments). 
 
@@ -119,7 +120,7 @@ Learn how to use dbt Cloud's features to help your team ship timely and quality
 
 If you want to run dbt jobs on a schedule, you can use tools such as dbt Cloud, Airflow, Prefect, Dagster, automation server, or Cron.-->
 
+## Related docs
 
-## Related docs 
-
+- [Use exports to materialize saved queries](/docs/use-dbt-semantic-layer/exports)
 - [Integrate with other orchestration tools](/docs/deploy/deployment-tools)

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

@@ -27,11 +27,11 @@ import AvailIntegrations from '/snippets/_sl-partner-links.md';
 
 ### Custom integration
 
-- You can create custom integrations using different languages and tools. We support connecting with JDBC, ADBC, and GraphQL APIs. For more info, check out [our examples on GitHub](https://github.com/dbt-labs/example-semantic-layer-clients/).
-- You can also connect to tools that allow you to write SQL. These tools must meet one of the two criteria:
-  
-    - Supports a generic JDBC driver option (such as DataGrip) or
-    - Uses Arrow Flight SQL JDBC driver version 12.0.0 or higher.
+- [Exports](/docs/use-dbt-semantic-layer/exports) enable custom integration with additional tools that don't natively connect with the dbt Semantic Layer, such as PowerBI.
+- Develop custom integrations using different languages and tools, supported through JDBC, ADBC, and GraphQL APIs. For more info, check out [our examples on GitHub](https://github.com/dbt-labs/example-semantic-layer-clients/).
+- Connect to any tool that supports SQL queries. These tools must meet one of the two criteria:
+    - Offers a generic JDBC driver option (such as DataGrip) or
+    - Is compatible Arrow Flight SQL JDBC driver version 12.0.0 or higher.
 
 ## Related docs
 

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

@@ -47,6 +47,12 @@ plan="dbt Cloud Team or Enterprise"
     link="/docs/use-dbt-semantic-layer/setup-sl"
     icon="dbt-bit"/>
 
+<Card
+    title="Materialize queries with exports"
+    body="Use exports to materialize commonly used queries directly within your data platform, on a schedule."
+    link="/docs/use-dbt-semantic-layer/exports"
+    icon="dbt-bit"/>
+
 <Card
     title="Architecture"
     body="Learn about the powerful components that make up the dbt Semantic Layer."