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Author: github-actions[bot]

website/docs/docs/build/incremental-microbatch.md

@@ -4,17 +4,13 @@ description: "Learn about the 'microbatch' strategy for incremental models."
 id: "incremental-microbatch"
 ---
 
-# About microbatch incremental models <Lifecycle status="beta" />
+:::info
 
-:::info Microbatch
-
-The new `microbatch` strategy is available in beta for [dbt Cloud "Latest"](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9. 
+Available for [dbt Cloud "Latest"](/docs/dbt-versions/cloud-release-tracks) and dbt Core v1.9 or higher.
 
 If you use a custom microbatch macro, set a [distinct behavior flag](/reference/global-configs/behavior-changes#custom-microbatch-strategy) in your `dbt_project.yml` to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the [microbatch strategy](#how-microbatch-compares-to-other-incremental-strategies).
 
-Read and participate in the discussion: [dbt-core#10672](https://github.com/dbt-labs/dbt-core/discussions/10672)
-
-Refer to [Supported incremental strategies by adapter](/docs/build/incremental-strategy#supported-incremental-strategies-by-adapter) for a list of supported adapters. 
+Read and participate in the discussion: [dbt-core#10672](https://github.com/dbt-labs/dbt-core/discussions/10672). Refer to [Supported incremental strategies by adapter](/docs/build/incremental-strategy#supported-incremental-strategies-by-adapter) for a list of supported adapters. 
 
 :::
 

website/docs/docs/build/incremental-models-overview.md

@@ -42,5 +42,5 @@ Transaction management, a process used in certain data platforms, ensures that a
 ## Related docs
 - [Incremental models](/docs/build/incremental-models) to learn how to configure incremental models in dbt.
 - [Incremental strategies](/docs/build/incremental-strategy) to understand how dbt implements incremental models on different databases.
-- [Microbatch](/docs/build/incremental-strategy) <Lifecycle status="beta" /> to understand a new incremental strategy intended for efficient and resilient processing of very large time-series datasets.
+- [Microbatch](/docs/build/incremental-strategy) to understand a new incremental strategy intended for efficient and resilient processing of very large time-series datasets.
 - [Materializations best practices](/best-practices/materializations/1-guide-overview) to learn about the best practices for using materializations in dbt.

website/docs/docs/build/incremental-strategy.md

@@ -13,7 +13,7 @@ There are various strategies to implement the concept of incremental materializa
 
 An optional `incremental_strategy` config is provided in some adapters that controls the code that dbt uses to build incremental models.
 
-:::info Microbatch <Lifecycle status="beta" />
+:::info Microbatch
 
 The [`microbatch` incremental strategy](/docs/build/incremental-microbatch) is intended for large time-series datasets. dbt will process the incremental model in multiple queries (or "batches") based on a configured `event_time` column. Depending on the volume and nature of your data, this can be more efficient and resilient than using a single query for adding new data.
 
@@ -25,7 +25,7 @@ This table represents the availability of each incremental strategy, based on th
 
 Click the name of the adapter in the below table for more information about supported incremental strategies.
 
-| Data platform adapter | `append` | `merge` | `delete+insert` | `insert_overwrite` | `microbatch` <Lifecycle status="beta"/> |
+| Data platform adapter | `append` | `merge` | `delete+insert` | `insert_overwrite` | `microbatch`        |
 |-----------------------|:--------:|:-------:|:---------------:|:------------------:|:-------------------:|
 | [dbt-postgres](/reference/resource-configs/postgres-configs#incremental-materialization-strategies) |     ✅    |    ✅   |        ✅        |                    |      ✅            |
 | [dbt-redshift](/reference/resource-configs/redshift-configs#incremental-materialization-strategies) |     ✅    |    ✅   |        ✅        |                    |      ✅        |
@@ -200,7 +200,7 @@ Before diving into [custom strategies](#custom-strategies), it's important to un
 | `delete+insert`        | `get_incremental_delete_insert_sql`    |
 | `merge`                | `get_incremental_merge_sql`            |
 | `insert_overwrite`     | `get_incremental_insert_overwrite_sql` |
-| `microbatch`  <Lifecycle status="beta"/>         | `get_incremental_microbatch_sql`       |
+| `microbatch`           | `get_incremental_microbatch_sql`       |
 
 
 For example, a built-in strategy for the `append` can be defined and used with the following files:

website/docs/docs/build/metricflow-time-spine.md

@@ -22,7 +22,7 @@ To see the generated SQL for the metric and dimension types that use time spine
 
 ## Configuring time spine in YAML
 
- Time spine models are normal dbt models with extra configurations that tell dbt and MetricFlow how to use specific columns by defining their properties. Add the [`models` key](/reference/model-properties) for the time spine in your `models/` directory. If your project already includes a calendar table or date dimension, you can configure that table as a time spine. Otherwise, review the [example time-spine tables](#example-time-spine-tables) to create one. If the relevant model file (`util/_models.yml`) doesn't exist, create it and add the configuration mentioned in the [next section](#creating-a-time-spine-table).
+ Time spine models are normal dbt models with extra configurations that tell dbt and MetricFlow how to use specific columns by defining their properties. Add the [`models` key](/reference/model-properties) for the time spine in your `models/` directory. If your project already includes a calendar table or date dimension, you can configure that table as a time spine. Otherwise, review the [example time-spine tables](#example-time-spine-tables) to create one. If the relevant model file doesn't exist, create it and add the configuration mentioned in the [next section](#creating-a-time-spine-table).
 
  Some things to note when configuring time spine models:
 
@@ -73,8 +73,6 @@ This example creates a time spine at an hourly grain and a daily grain: `time_sp
 </File>
 </VersionBlock>
 
-<Lightbox src="/img/time_spines.png" width="50%" title="Time spine directory structure" />
-
 <!--
 <VersionBlock lastVersion="1.8">
 <File name="models/_models.yml">

website/docs/docs/cloud/about-cloud/change-your-dbt-cloud-theme.md

@@ -6,19 +6,16 @@ sidebar_label: Change your dbt Cloud theme
 image: /img/docs/dbt-cloud/using-dbt-cloud/light-vs-dark.png
 ---
 
-# Change your dbt Cloud theme <Lifecycle status="preview" />
+# Change your dbt Cloud theme 
 
-dbt Cloud supports **Light mode** (default), **Dark mode**, and **System mode** (respects your browser's theme for light or dark mode) under the **Theme** section of your user profile. You can seamlessly switch between these modes directly from the profile menu, customizing your viewing experience.
+dbt Cloud supports **Light mode** (default), **Dark mode**, and **System mode** (respects your browser's theme for light or dark mode) under the **Theme** section of your user profile and is available for all [plans](https://www.getdbt.com/pricing). 
+
+You can seamlessly switch between these modes directly from the profile menu, customizing your viewing experience.
 
 Your selected theme is stored in your user profile, ensuring a consistent experience across dbt Cloud.
 
 Theme selection applies across all areas of dbt Cloud, including the [IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud), [dbt Explorer](/docs/collaborate/explore-projects), [environments](/docs/environments-in-dbt), [jobs](/docs/deploy/jobs), and more. Learn more about customizing themes in [Change themes in dbt Cloud](/docs/cloud/about-cloud/change-your-dbt-cloud-theme#change-themes-in-dbt-cloud).
 
-## Prerequisites
-
-- You have a dbt Cloud account. If you don’t, try [dbt Cloud for free!](https://www.getdbt.com/signup)
-- Dark mode is currently available on the Developer plan and will gradually be made available for all [plans](https://www.getdbt.com/pricing) in the future. Stay tuned for updates.
-
 ## Change themes in dbt Cloud
 
 To switch to dark mode in the dbt Cloud UI, follow these steps:

website/docs/docs/collaborate/data-tile.md

@@ -15,7 +15,13 @@ The data health tile:
 - Provides richer information and makes it easier to debug.
 - Revamps the existing, [job-based tiles](#job-based-data-health).
 
-Data health tiles rely on [exposures](/docs/build/exposures) to surface data health signals in your dashboards. When you configure exposures in your dbt project, you are explicitly defining how specific outputs—like dashboards or reports—depend on your data models.
+Data health tiles rely on [exposures](/docs/build/exposures) to surface data health signals in your dashboards. An exposure defines how specific outputs — like dashboards or reports — depend on your data models. Exposures in dbt can be configured in two ways:
+
+- Manual exposures — Defined manually and explicitly in your project’s YAML files.
+- Auto exposures — Pulled automatically from your BI tool as long as your BI tool integrates with dbt Cloud and you have access to [auto-exposures](/docs/collaborate/auto-exposures). 
+  - dbt Cloud creates auto exposures automatically for dbt Cloud users with access to this feature, removing the need for manual YAML definitions.
+  - dbt Cloud pulls exposure metadata from your BI tool and integrates it into dbt Explorer.
+  - These auto exposures are stored in dbt’s metadata system, appear in dbt Explorer, and behave like manual exposures, however they don’t exist in YAML files.
 
 <DocCarousel slidesPerView={1}>
 <Lightbox src="/img/docs/collaborate/dbt-explorer/data-tile-pass.jpg" width="60%" title="Example of passing Data health tile in your dashboard." />
@@ -27,8 +33,11 @@ Data health tiles rely on [exposures](/docs/build/exposures) to surface data hea
 - You must have a dbt Cloud account on a [Team or Enterprise plan](https://www.getdbt.com/pricing/).
 - You must be an account admin to set up [service tokens](/docs/dbt-cloud-apis/service-tokens#permissions-for-service-account-tokens).
 - You must have [develop permissions](/docs/cloud/manage-access/seats-and-users).
-- Have [exposures](/docs/build/exposures) defined in your project and [source freshness](/docs/deploy/source-freshness) enabled in the job that generates this exposure.
-   - The exposure used for the  data health tile must have the [`type` property](/docs/build/exposures#available-properties) set to `dashboard`. Otherwise, you won't be able to view the **Embed data health tile in your dashboard** dropdown in dbt Explorer.
+- You have [exposures](/docs/build/exposures) defined in your project:
+  -  If using manual exposures, they must be explicitly defined in your YAML files.
+  -  If using auto exposures, ensure your BI tool is [configured](/docs/cloud-integrations/configure-auto-exposures) with dbt Cloud.
+- You have [source freshness](/docs/deploy/source-freshness) enabled in the job that generates this exposure.
+- The exposure used for the  data health tile must have the [`type` property](/docs/build/exposures#available-properties) set to `dashboard`. Otherwise, you won't be able to view the **Embed data health tile in your dashboard** dropdown in dbt Explorer.
 
 ## View exposure in dbt Explorer
 

website/package-lock.json

@@ -1,20 +1,38 @@
-## View auto-exposures in dbt Explorer
+## View auto-exposures
 
-After setting up auto-exposures in dbt Cloud, you can view them in dbt Explorer for a richer experience:
-1. Navigate to dbt Explorer by clicking on the **Explore** link in the navigation.
-2. From the **Overview** page, you can view auto-exposures from a couple of places:
-   - From the **Exposures** menu item under **Resources**. This menu provides a comprehensive list of all the exposures so you can quickly access and manage them.
-   <Lightbox src="/img/docs/cloud-integrations/auto-exposures/explorer-view-resources.jpg" width="120%" title="View from the dbt Explorer under the 'Resources' menu."/>
+After setting up auto-exposures in dbt Cloud, you can view them in dbt Explorer for a richer experience.
 
-   - Locate directly from within the **File tree** under the **imported_from_tableau** sub-folder. This view integrates exposures seamlessly with your project files, making it easy to find and reference them from your project's structure.
-   <Lightbox src="/img/docs/cloud-integrations/auto-exposures/explorer-view-file-tree.jpg" width="120%" title="View from the dbt Explorer under the 'File tree' menu."/>
+Navigate to dbt Explorer by clicking on the **Explore** link in the navigation. From the **Overview** page, you can view auto-exposures from a couple of places:
+- [Exposures menu](#exposures-menu)
+- [File tree](#file-tree)
+- [Project lineage](#project-lineage)
 
-   - From the **Project lineage** view, which visualizes the dependencies and relationships in your project. Exposures are represented with the Tableau icon, offering an intuitive way to see how they fit into your project's overall data flow.
+### Exposures menu
+View auto exposures from the **Exposures** menu item under **Resources**. This menu provides a comprehensive list of all the exposures so you can quickly access and manage them. The menu displays the following information:
+   - **Name**: The name of the exposure.
+   - **Health**: The [data health signal](/docs/collaborate/data-health-signals) of the exposure.
+   - **Type**: The type of exposure, such as `dashboard` or `notebook`.
+   - **Owner**: The owner of the exposure.
+   - **Owner email**: The email address of the owner of the exposure.
+   - **Integration**: The BI tool that the exposure is integrated with.
+   - **Exposure mode**: The type of exposure defined: **Auto** or **Manual**. Exposures in dbt can be configured in two ways:
+     - **Manual** &mdash; [Defined](/docs/build/exposures) manually and explicitly in your project’s YAML files.
+     - **Auto** &mdash; Pulled automatically from your BI tool as long as your BI tool integrates with dbt Cloud and you have access to auto-exposures.
+     - dbt Cloud automatically creates auto exposures for users with access, removing the need for manual YAML definitions. These auto exposures are stored in dbt’s metadata system, appear in dbt Explorer, and behave like manual exposures, however they don’t exist in YAML files.
+<Lightbox src="/img/docs/cloud-integrations/auto-exposures/explorer-view-resources.jpg" width="120%" title="View from the dbt Explorer under the 'Resources' menu."/>
 
-   <DocCarousel slidesPerView={1}>
+### File tree
+Locate directly from within the **File tree** under the **imported_from_tableau** sub-folder. This view integrates exposures seamlessly with your project files, making it easy to find and reference them from your project's structure.
 
-   <Lightbox src="/img/docs/cloud-integrations/auto-exposures/explorer-lineage2.jpg" width="95%" title="View from the dbt Explorer in your Project lineage view, displayed with the Tableau icon."/>
+<Lightbox src="/img/docs/cloud-integrations/auto-exposures/explorer-view-file-tree.jpg" width="120%" title="View from the dbt Explorer under the 'File tree' menu."/>
 
-   <Lightbox src="/img/docs/cloud-integrations/auto-exposures/explorer-lineage.jpg" width="95%" title="View from the dbt Explorer in your Project lineage view, displayed with the Tableau icon."/>
+### Project lineage
+From the **Project lineage** view, which visualizes the dependencies and relationships in your project. Exposures are represented with the Tableau icon, offering an intuitive way to see how they fit into your project's overall data flow.
 
-   </DocCarousel>
+<DocCarousel slidesPerView={1}>
+
+<Lightbox src="/img/docs/cloud-integrations/auto-exposures/explorer-lineage2.jpg" width="95%" title="View from the dbt Explorer in your Project lineage view, displayed with the Tableau icon."/>
+
+<Lightbox src="/img/docs/cloud-integrations/auto-exposures/explorer-lineage.jpg" width="95%" title="View from the dbt Explorer in your Project lineage view, displayed with the Tableau icon."/>
+
+</DocCarousel>