Merge branch 'current' into mirnawong1-patch-5

website/docs/docs/cloud/manage-access/set-up-bigquery-oauth.md

@@ -74,3 +74,7 @@ You will then be redirected to BigQuery and asked to approve the drive, cloud pl
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/dbt-cloud-enterprise/BQ-auth/BQ-access.png" title="BigQuery access request" />
 
 Select **Allow**. This redirects you back to dbt Cloud. You should now be an authenticated BigQuery user, ready to use the dbt Cloud IDE.
+
+## FAQs
+
+<FAQ path="Warehouse/bg-oauth-drive-scope"/>

website/docs/docs/collaborate/cloud-build-and-view-your-docs.md

@@ -40,16 +40,17 @@ To create and schedule documentation-only jobs at the end of your production job
 You configure project documentation to generate documentation when the job you set up in the previous section runs. In the project settings, specify the job that generates documentation artifacts for that project. Once you configure this setting, subsequent runs of the job will automatically include a step to generate documentation.
 
 1. Click the gear icon in the top right.
-2. Select **Projects** and click the project that needs documentation.
-3. Click **Edit**.
-4. Under "Artifacts," select the job that should generate docs when it runs.
+2. Select **Account Settings**.
+3. Navigate to **Projects** and select the project that needs documentation.
+4. Click **Edit**.
+5. Under **Artifacts**, select the job that should generate docs when it runs.
    <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/documentation-project-details.png" title="Configuring project documentation"/>
-5. Click **Save**.
+6. Click **Save**.
 
 ## Generating documentation
 
-To generate documentation in the IDE, run the `dbt docs generate` command in the
-Command Bar in the IDE. This command will generate the Docs for your dbt project as it exists in development in your IDE session.
+To generate documentation in the dbt Cloud IDE, run the `dbt docs generate` command in the
+Command Bar in the dbt Cloud IDE. This command will generate the Docs for your dbt project as it exists in development in your IDE session.
 
 <Lightbox src="/img/docs/dbt-cloud/dbt-docs-generate-command.png" title="dbt docs generate"/>
 

website/docs/docs/core/connect-data-platform/alloydb-setup.md

@@ -3,7 +3,7 @@ title: "AlloyDB setup"
 meta:
   maintained_by: Community?
   authors: 'dbt-labs'
-  github_repo: 'dbt-labs/dbt-postgres'
+  github_repo: 'dbt-labs/dbt-core'
   pypi_package: 'dbt-postgres'
   min_core_version: 'v1.0.0'
   cloud_support: Not Supported

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

@@ -25,7 +25,6 @@ import AvailIntegrations from '/snippets/_sl-partner-links.md';
 You can also integrate the following tools with the dbt Semantic Layer:
 - [Push.ai](https://docs.push.ai/semantic-layer-integrations/dbt-semantic-layer)
 - [Delphi](delphihq.com)
-- KlipFolio Power Metrics - Documentation coming soon
 
 ### Custom integration
 

website/docs/faqs/Environments/custom-branch-settings.md

@@ -1,7 +1,7 @@
 ---
-title: How do I use the `Custom Branch` settings in a dbt Cloud Environment?
+title: How do I use the 'Custom Branch' settings in a dbt Cloud Environment?
 description: "Use custom code from your repository"
-sidebar_label: 'Custom Branch settings'
+sidebar_label: 'Custom branch settings'
 id: custom-branch-settings
 ---
 
@@ -15,12 +15,21 @@ To specify a custom branch:
 
 ## Development
 
-In a development environment, the default branch (commonly the `main` branch) is a read-only branch found in the IDE's connected repositories, which you can use to create development branches. Identifying a custom branch overrides this default behavior. Instead, your custom branch becomes read-only and can be used to create development branches. You will no longer be able to make commits to the custom branch from within the dbt Cloud IDE.
+In a development environment, the default branch (usually named `main`) is a read-only branch in your connected repositories, which allows you to create new branches for development from it.  
 
-For example, you can use the `develop` branch of a connected repository. Edit an environment, select  **Only run on a custom branch** in **General settings** , enter **develop** as the name of your custom branch.
+Specifying a **Custom branch** overrides the default behavior. It makes the custom branch 'read-only' and enables you to create new development branches from it. This also means you can't edit this custom branch directly.
 
-<Lightbox src="/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/dev-environment-custom-branch.png" title="Configuring a custom base repository branch"/>
+Only one branch can be read-only, which means when you set up a custom branch, your `main` branch (usually read-only) becomes editable. If you want to protect the `main` branch and prevent any commits on it, you need to set up branch protection rules in your git provider settings. This ensures your `main` branch remains secure and no new commits can be made to it.
+
+For example, if you want to use the `develop` branch of a connected repository:
+
+- Go to an environment and select **Settings** to edit it
+- Select  **Only run on a custom branch** in **General settings**
+- Enter **develop** as the name of your custom branch
+- Click **Save**
+
+<Lightbox src="/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/dev-environment-custom-branch.png" width="70%" title="Configuring a custom base repository branch"/>
 
 ## Deployment
 
-When running jobs in a deployment environment, dbt will clone your project from your connected repository before executing your models. By default, dbt uses the default branch of your repository (commonly the `main` branch). To specify a different version of your project for dbt to execute during job runs in a particular environment, you can edit the Custom Branch setting as shown in the previous steps. 
+When running jobs in a deployment environment, dbt will clone your project from your connected repository before executing your models. By default, dbt uses the default branch of your repository (commonly the `main` branch). To specify a different version of your project for dbt to execute during job runs in a particular environment, you can edit the Custom Branch setting as shown in the previous steps. 

website/docs/faqs/Warehouse/bq-oauth-drive-scope.md

@@ -0,0 +1,8 @@
+---
+title: Why does the BigQuery OAuth application require scopes to Google Drive?
+description: "Learn more about Google Drive scopes in the BigQuery OAuth application"
+sidebar_label: "BigQuery OAuth Drive Scopes"
+id: bq-oauth-drive-scope
+---
+
+BigQuery supports external tables over both personal Google Drive files and shared files. For more information, refer to [Create Google Drive external tables](https://cloud.google.com/bigquery/docs/external-data-drive).

website/docs/quickstarts/bigquery-qs.md

@@ -88,25 +88,22 @@ In order to let dbt connect to your warehouse, you'll need to generate a keyfile
 4. Click **Upload a Service Account JSON File** in settings.
 5. Select the JSON file you downloaded in [Generate BigQuery credentials](#generate-bigquery-credentials) and dbt Cloud will fill in all the necessary fields.
 6. Click **Test Connection**. This verifies that dbt Cloud can access your BigQuery account.
-7. Click **Next** if the test succeeded. If it failed, you might need to go back and regenerate your BigQuery credentials.
+7. Click **Next** if the test succeeds. If it fails, you might need to go back and regenerate your BigQuery credentials.
 
 
 ## Set up a dbt Cloud managed repository 
 <Snippet path="tutorial-managed-repo" />
 
 
-## Initialize your dbt project​ and start developing
+## Initialize your dbt project
 Now that you have a repository configured, you can initialize your project and start development in dbt Cloud:
 
 1. Click **Start developing in the IDE**. It might take a few minutes for your project to spin up for the first time as it establishes your git connection, clones your repo, and tests the connection to the warehouse.
 2. Above the file tree to the left, click **Initialize dbt project**. This builds out your folder structure with example models.
 3. Make your initial commit by clicking **Commit and sync**. Use the commit message `initial commit` and click **Commit**. This creates the first commit to your managed repo and allows you to open a branch where you can add new dbt code.
 4. You can now directly query data from your warehouse and execute `dbt run`. You can try this out now:
-    - Click **+ Create new file**, add this query to the new file, and click **Save as** to save the new file:  
-        ```sql
-        select * from `dbt-tutorial.jaffle_shop.customers`
-        ```
     - In the command line bar at the bottom, enter `dbt run` and click **Enter**. You should see a `dbt run succeeded` message.
+    - To confirm the success of the above command, navigate to the BigQuery Console and discover the newly created models.
 
 ## Build your first model
 1. Under **Version Control** on the left, click **Create branch**. You can name it `add-customers-model`. You need to create a new branch since the main branch is set to read-only mode.
@@ -174,7 +171,7 @@ select * from final
 
 6. Enter `dbt run` in the command prompt at the bottom of the screen. You should get a successful run and see the three models.
 
-Later, you can connect your business intelligence (BI) tools to these views and tables so they only read cleaned up data rather than raw data in your BI tool.
+Later, you can connect your business intelligence (BI) tools to these views and tables so they only read cleaned-up data rather than raw data in your BI tool.
 
 #### FAQs
 
@@ -282,7 +279,7 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 4. Execute `dbt run`.
 
-    This time, when you performed a `dbt run`, separate views/tables were created for `stg_customers`, `stg_orders` and `customers`. dbt inferred the order to run these models. Because `customers` depends on `stg_customers` and `stg_orders`, dbt builds `customers` last. You do not need to explicitly define these dependencies.
+    This time, when you performed a `dbt run`, separate views/tables were created for `stg_customers`, `stg_orders`, and `customers`. dbt inferred the order to run these models. Because `customers` depends on `stg_customers` and `stg_orders`, dbt builds `customers` last. You do not need to explicitly define these dependencies.
 
 #### FAQs {#faq-2}
 

website/snippets/_sl-partner-links.md

@@ -28,6 +28,20 @@ The following tools integrate with the dbt Semantic Layer:
     </a>
   </div>
 
+<div className="card-container">
+  <Card
+    title="KlipFolio Power Metrics"
+    body="Refer to KlipFolio's documentation to learn how to connect, and deliver a streamlined metrics catalog to your business users."
+    link="https://support.klipfolio.com/hc/en-us/articles/18164546900759-PowerMetrics-Adding-dbt-Semantic-Layer-metrics"
+    icon="klipfolio"/>
+    <a href="https://support.klipfolio.com/hc/en-us/articles/18164546900759-PowerMetrics-Adding-dbt-Semantic-Layer-metrics"
+    className="external-link"
+      target="_blank"
+      rel="noopener noreferrer">
+      <i className="fa fa-external-link"></i>
+    </a>
+</div>
+
 <div className="card-container">
   <Card
     title="Lightdash"