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

@@ -8,16 +8,18 @@ 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:
+They are distinct from [Exports](/docs/use-dbt-semantic-layer/exports), which are scheduled and executed saved queries using [dbt's job scheduler](/docs/deploy/job-scheduler).
 
- 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 |
+| Feature      | Saved queries  | Exports    |
+|--------------|----------------|------------|
+| **Availability**   | Available in both dbt Core and dbt Cloud.  | Available to dbt Cloud users on Team and Enterprise plans. | 
+| **Purpose**    | To define and manage common Semantic Layer queries.     | Automatically runs saved queries and materializes them within your data platform using dbt's job scheduler. |
+| **Usage**   | For organizing and reusing queries within dbt projects. | For materializing query results in the data platform.  |
 
 The following is an example of a saved query:
 
+All metrics in a saved query need to use the same dimensions in the `group_by` or `where` clauses.
+
 ```yaml
 saved_queries:
   name: p0_booking
@@ -33,6 +35,27 @@ saved_queries:
       - "{{ Dimension('listing__capacity_latest') }} > 3"
 ```
 
-### FAQs 
+## 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 CLI. |
+| `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.  |
+
+
+## Related docs
 
-* All metrics in a saved query need to use the same dimensions in the `group_by` or `where` clauses.
+- [Exports](/docs/use-dbt-semantic-layer/exports)
+- [Set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl)

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). 
 

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 saved queries within the 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."

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

@@ -0,0 +1,117 @@
+---
+title: "Exports"
+description: "Use Exports to materialize tables to the data platform on a schedule."
+sidebar_label: "Materialize queries with Exports"
+---
+
+Exports in the dbt Semantic Layer extends the [saved queries](/docs/build/saved-queries) functionality:
+
+- Exports enable you to materialize and automate these queries within your data platform.
+- Exports uses the dbt Cloud job scheduler to execute saved queries for reliable and fast data reporting.
+
+While saved queries are a way to save and reuse commonly used queries in MetricFlow, Exports takes this a step further by using the MetricFlow server to materialize these queries as data tables of views using the dbt Cloud job scheduler.
+
+| Feature    | Exports  | Saved queries  |
+|-----------|-----------|----------------|
+| **Availability**    | Available to dbt Cloud users on [Team or Enterprise](https://www.getdbt.com/pricing/) plan on  on dbt versions 1.7 or higher.| Available in both dbt Core and dbt Cloud.     |
+| **Purpose**         | To schedule and run saved queries as part of [dbt's job scheduler](/docs/deploy/job-scheduler). | 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. <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 />**Example**: Standardizing a frequently used revenue calculation across multiple reports. | 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 dbt 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.   |
+
+## Define exports
+
+Exports are essentially saved queries that you can schedule and execute using [dbt's job scheduler](/docs/deploy/job-scheduler) capabilities. Exports include the following materialization types: 
+
+- `table` (available now) 
+- and `window_table`, `incremental_table`, and `file` (coming soon)
+
+You can define Exports in a YAML format in the same file as saved queries:
+
+```yaml
+saved_queries:
+  - name: my_query
+    description: null
+    query_params:
+      metrics:
+        - null
+      group_bys:
+        - TimeDimension()
+        - ...
+      where:
+        - null
+    exports:
+      - name: my_query
+        config:
+          export_as: table # options: table, view
+          schema: my_schema # [optional - DEFAULT to deployment schema]
+          alias: some_table_name # [optional - DEFAULT to export name]
+```
+
+## Parameters
+
+By default all exports are run for a saved query. You can select a specific export with the `select` flag or you can specify a new export using the `export-as` flag. The Job scheduler runs the equivalent of` dbt sl export --saved-query sq_name`.
+
+| Parameters | Type    | Required | Description    |
+| ------- | --------- | ---------- | ---------------- |
+| `saved-query` | String    | Required     | A name of a saved query that could be used.    |
+| `select` | List or String   | Optional    | Names of exports to be selected from the saved query. |
+| `exclude` | String  | Optional    | Not the names of exports to be selected from the saved query. |
+| `export-as` | String  | Optional    | Type of export to create from the export_as types available in the config. |
+| `schema` | String  | Optional    | Schema to use for creating the table or view. |
+| `alias` | String  | Optional    | Table alias to use to write the table or view. |
+
+## Schedule Exports
+
+Exports in dbt are effectively integrated with your data models and metrics, allowing you to automate and materialize saved queries using the dbt Cloud job scheduler. These exports, along with saved queries, are treated as standard dbt models and are executed as part of the dbt <Term id="dag" />, following model builds where dependencies exist.
+
+To execute exports using the job scheduler:
+
+- Create a [deploy job](/docs/deploy/deploy-jobs) in dbt Cloud. 
+- Use the built-in `dbt build` command to execute exports and saved queries. Note that although you can selectively run specific saved queries, this level of selection isn't available for individual exports. However, you can always create a different saved query if you want to separate exports. WHAT'S A SAMPLE QUERY? IS IT `export-as export_name` OR `dbt sl export --saved-query sq_name`
+- After dbt completes building the models, the MetricFlow Server processes the exports, compiles the necessary datasets, and executes data operations.
+- You can review the exports execution details in the jobs logs. Since saved queries are integrated into the dbt DAG, all outputs related to exports are available in the `dbt build` logs.
+- Your data is now available in the data platform for querying.
+
+   <Lightbox src="/img/docs/dbt-cloud/semantic-layer/saved_queries_run.jpg" width="90%" title="TeThe st"/>
+
+
+## Caching
+
+Caching reduces load times and costs by pre-computing and storing frequently queried datasets. This feature is critical for companies with large datasets or requiring fast query compute times.
+
+There are two types of caching:
+- Result cache &mdash; Cache all queries in the data platform and configured at the project or environment-level. This cache is used to store the results of a query save costs, and speed up subsequent queries.
+- Declarative cache &mdash; Saved queries specified by the user and defaults to all saved queries being cached or not. This cache enables more control over what is cached.
+
+To configure caching, you can add the `config` property to your `saved_queries` configuration:
+
+```yaml
+saved_queries:
+  - name: my_query
+    description:
+    query_params:
+      ...
+    exports:
+      ...
+    config:
+      cache:
+        enabled: true|false
+```
+
+### Parameters
+
+| GraphQL	| dbt Cloud CLI |	ADBC| Description	|
+| --- | --- | --- | --- |
+| `create-query` | `dbt sl query` |	`{{ semantic_layer.query() }}` | Allows you to build the SQL for a query that does not hit the cache, utilizes a saved-query to specify parameters or specifies whether the cache should be utilized or overwritten.
+| `drop-cache` |	`dbt sl cache drop` |	`{{ semantic_layer.drop_cache() }}` |	Drops datasets from the cache using parameters which target specific object |
+| `inspect-cache` |	`dbt sl cache inspect` |	`{{ semantic_layer.inspect_cache() }}` |	Inspect and provides a list of objects that are in the cache. | 
+
+## Job commands?
+
+Additional parameters and commands are introduced in MetricFlow CLI and Cloud Interfaces to support Saved Queries and Exports functionalities.
+
+| GraphQL	| dbt Cloud CLI |	ADBC	| Description	| 	
+| --- | --- | --- | --- |
+| `create-query` |	`dbt sl query` |	`{{ semantic_layer.query() }}` |	Allows you to build the SQL for a query that does not hit the cache, utilizes a saved-query to specify parameters or specifies whether the cache should be utilized or overwritten	|
+| `export`	| `dbt sl export`	| None, Not possible	| Builds an export using the GraphQL API. Use the export [parameters](#parameters). specified above in  |

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

@@ -44,7 +44,7 @@ The following table compares the features available in dbt Cloud and source avai
 | Query dimension, entity, and metric metadata  through the CLI | ✅ | ✅ |
 | Query metrics and dimensions through semantic APIs (ADBC, GQL)  | ❌ | ✅ |
 | Connect to downstream integrations (Tableau, Hex, Mode, Google Sheets, and so on.) | ❌ | ✅ |
-| Create and run Exports to save metrics queries as tables in your data platform. | ❌ | Coming soon |
+| Create and run Exports to save metrics queries as tables in your data platform. | ❌ | ✅ |
 
 ## FAQs
 

website/sidebars.js

@@ -476,6 +476,7 @@ const sidebarSettings = {
         "docs/use-dbt-semantic-layer/dbt-sl",
         "docs/use-dbt-semantic-layer/quickstart-sl",
         "docs/use-dbt-semantic-layer/setup-sl",
+        "docs/use-dbt-semantic-layer/exports",
         "docs/use-dbt-semantic-layer/sl-architecture",
         {
           type: "category",