Merge branch 'current' into patch-4

contributing/content-style-guide.md

@@ -284,7 +284,7 @@ If the list starts getting lengthy and dense, consider presenting the same conte
 
 A bulleted list with introductory text:    
 
-> A dbt project is a directory of `.sql` and .yml` files. The directory must contain at a minimum:
+> A dbt project is a directory of `.sql` and `.yml` files. The directory must contain at a minimum:
 >
 > - Models: A model is a single `.sql` file. Each model contains a single `select` statement that either transforms raw data into a dataset that is ready for analytics or, more often, is an intermediate step in such a transformation.
 > - A project file: A `dbt_project.yml` file, which configures and defines your dbt project.

website/docs/docs/build/semantic-models.md

@@ -43,7 +43,7 @@ semantic_models:
   - name: the_name_of_the_semantic_model      ## Required
     description: same as always               ## Optional
     model: ref('some_model')                  ## Required
-    default:                                  ## Required
+    defaults:                                 ## Required
       agg_time_dimension: dimension_name    ## Required if the model contains dimensions
     entities:                                 ## Required
        - see more information in entities

website/docs/docs/cloud/about-cloud/regions-ip-addresses.md

@@ -11,8 +11,8 @@ dbt Cloud is [hosted](/docs/cloud/about-cloud/architecture) in multiple regions
 
 | Region | Location | Access URL | IP addresses | Developer plan | Team plan | Enterprise plan |
 |--------|----------|------------|--------------|----------------|-----------|-----------------|
-| North America multi-tenant [^1] | AWS us-east-1 (N. Virginia) | cloud.getdbt.com | 52.45.144.63 <br /> 54.81.134.249 <br />52.22.161.231 | ✅ | ✅ | ✅ |
-| North America Cell 1 [^1]       | AWS us-east-1 (N.Virginia) | {account prefix}.us1.dbt.com | [Located in Account Settings](#locating-your-dbt-cloud-ip-addresses) | ❌ | ❌ | ✅ |
+| North America multi-tenant [^1] | AWS us-east-1 (N. Virginia) | cloud.getdbt.com | 52.45.144.63 <br /> 54.81.134.249 <br />52.22.161.231 <br />52.3.77.232 <br />3.214.191.130 <br />34.233.79.135 | ✅ | ✅ | ✅ |
+| North America Cell 1 [^1]       | AWS us-east-1 (N. Virginia) | {account prefix}.us1.dbt.com | 52.45.144.63 <br /> 54.81.134.249 <br />52.22.161.231 <br />52.3.77.232 <br />3.214.191.130 <br />34.233.79.135 | ❌ | ❌ | ✅ |
 | EMEA [^1] | AWS eu-central-1	(Frankfurt) | emea.dbt.com | 3.123.45.39 <br /> 3.126.140.248 <br /> 3.72.153.148 | ❌ | ❌ | ✅ |
 | APAC  [^1] | 	AWS ap-southeast-2  (Sydney)| au.dbt.com | 52.65.89.235 <br /> 3.106.40.33 <br /> 13.239.155.206 <br />| ❌ | ❌ | ✅ |
 | Virtual Private dbt or Single tenant | Customized |  Customized | Ask [Support](/community/resources/getting-help#dbt-cloud-support) for your IPs | ❌ | ❌ | ✅ |

website/docs/docs/cloud/secure/about-privatelink.md

@@ -23,3 +23,4 @@ dbt Cloud supports the following data platforms for use with the PrivateLink fea
 - [Databricks](/docs/cloud/secure/databricks-privatelink)
 - [Redshift](/docs/cloud/secure/redshift-privatelink)
 - [Postgres](/docs/cloud/secure/postgres-privatelink)
+- [VCS](/docs/cloud/secure/vcs-privatelink)

website/docs/docs/collaborate/explore-projects.md

@@ -2,7 +2,7 @@
 title: "Explore your dbt projects"
 sidebar_label: "Explore dbt projects"
 description: "Learn about dbt Explorer and how to interact with it to understand, improve, and leverage your data pipelines."
-pagination_next: "docs/collaborate/explore-multiple-projects"
+pagination_next: "docs/collaborate/model-performance"
 pagination_prev: null
 ---
 
@@ -36,7 +36,7 @@ For a richer experience with dbt Explorer, you must:
 - Run [dbt source freshness](/reference/commands/source#dbt-source-freshness) within a job in the environment to view source freshness data.
 - Run [dbt snapshot](/reference/commands/snapshot) or [dbt build](/reference/commands/build) within a job in the environment to view snapshot details.
 
-Richer and more timely metadata will become available as dbt, the Discovery API, and the underlying dbt Cloud platform evolves.
+Richer and more timely metadata will become available as dbt Core, the Discovery API, and the underlying dbt Cloud platform evolves.
 
 ## Explore your project's lineage graph {#project-lineage}
 
@@ -46,6 +46,8 @@ If you don't see the project lineage graph immediately, click **Render Lineage**
 
 The nodes in the lineage graph represent the project’s resources and the edges represent the relationships between the nodes. Nodes are color-coded and include iconography according to their resource type.
 
+By default, dbt Explorer shows the project's [applied state](/docs/dbt-cloud-apis/project-state#definition-logical-vs-applied-state-of-dbt-nodes) lineage. That is, it shows models that have been successfully built and are available to query, not just the models defined in the project.
+
 To explore the lineage graphs of tests and macros, view [their resource details pages](#view-resource-details). By default, dbt Explorer excludes these resources from the full lineage graph unless a search query returns them as results.
 
 To interact with the full lineage graph, you can:

website/docs/docs/collaborate/model-performance.md

@@ -0,0 +1,41 @@
+---
+title: "Model performance"
+sidebar_label: "Model performance"
+description: "Learn about ."
+---
+
+dbt Explorer provides metadata on dbt Cloud runs for in-depth model performance and quality analysis. This feature assists in reducing infrastructure costs and saving time for data teams by highlighting where to fine-tune projects and deployments — such as model refactoring or job configuration adjustments.
+
+<LoomVideo id='98f33b3b7a374df0b7c04747eae6ef44' />
+
+:::tip Beta 
+
+The model performance beta feature is now available in dbt Explorer! Check it out! 
+:::
+
+## The Performance overview page 
+
+You can pinpoint areas for performance enhancement by using the Performance overview page. This page presents a comprehensive analysis across all project models and displays the longest-running models, those most frequently executed, and the ones with the highest failure rates during runs/tests. Data can be segmented by environment and job type which can offer insights into:
+
+- Most executed models (total count).
+- Models with the longest execution time (average duration).
+- Models with the most failures, detailing run failures (percentage and count) and test failures (percentage and count).
+
+Each data point links to individual models in Explorer. 
+
+<Lightbox src="/img/docs/collaborate/dbt-explorer/example-performance-overview-page.png" width="80%" title="Example of Performance overview page"/>
+
+You can view historical metadata for up to the past three months. Select the time horizon using the filter, which defaults to a two-week lookback.
+
+<Lightbox src="/img/docs/collaborate/dbt-explorer/ex-2-week-default.png" title="Example of dropdown"/>
+
+## The Model performance tab
+
+You can view trends in execution times, counts, and failures by using the Model performance tab for historical performance analysis. Daily execution data includes:
+
+- Average model execution time.
+- Model execution counts, including failures/errors (total sum).
+
+Clicking on a data point reveals a table listing all job runs for that day, with each row providing a direct link to the details of a specific run.
+
+<Lightbox src="/img/docs/collaborate/dbt-explorer/example-model-performance-tab.png" title="Example of the Model performance tab"/>

website/docs/docs/collaborate/project-recommendations.md

@@ -0,0 +1,50 @@
+---
+title: "Project recommendations"
+sidebar_label: "Project recommendations"
+description: "dbt Explorer provides recommendations that you can take to improve the quality of your dbt project."
+---
+
+:::tip Beta 
+
+The project recommendations beta feature is now available in dbt Explorer! Check it out!
+
+:::
+
+dbt Explorer provides recommendations about your project from the `dbt_project_evaluator` [package](https://hub.getdbt.com/dbt-labs/dbt_project_evaluator/latest/) using metadata from the Discovery API.
+
+Explorer also offers a global view, showing all the recommendations across the project for easy sorting and summarizing. 
+
+These recommendations provide insight into how you can build a more well documented, well tested, and well built project, leading to less confusion and more trust.
+
+The Recommendations overview page includes two top-level metrics measuring the test and documentation coverage of the models in your project. 
+
+- **Model test coverage** — The percent of models in your project (models not from a package or imported via dbt Mesh) with at least one dbt test configured on them.
+- **Model documentation coverage** — The percent of models in your project (models not from a package or imported via dbt Mesh) with a description.
+
+<Lightbox src="/img/docs/collaborate/dbt-explorer/example-recommendations-overview.png" width="80%" title="Example of the Recommendations overview page with project metrics and the recommendations for all resources in the project"/>
+
+## List of rules
+
+| Category | Name | Description | Package Docs Link |
+| --- | --- | --- | --- |
+| Modeling | Direct Join to Source | Model that joins both a model and source, indicating a missing staging model | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/modeling/#direct-join-to-source) |
+| Modeling | Duplicate Sources | More than one source node corresponds to the same data warehouse relation | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/modeling/#duplicate-sources) |
+| Modeling | Multiple Sources Joined  | Models with more than one source parent, indicating lack of staging models | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/modeling/#multiple-sources-joined) |
+| Modeling | Root Model | Models with no parents, indicating potential hardcoded references and need for sources  | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/modeling/#root-models) |
+| Modeling | Source Fanout | Sources with more than one model child, indicating a need for staging models | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/modeling/#source-fanout) |
+| Modeling | Unused Source | Sources that are not referenced by any resource | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/modeling/#unused-sources) |
+| Performance | Exposure Dependent on View | Exposures with at least one model parent materialized as a view, indicating potential query performance issues | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/performance/#exposure-parents-materializations) |
+| Testing | Missing Primary Key Test | Models with insufficient testing on the grain of the model.  | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/testing/#missing-primary-key-tests) |
+| Documentation | Undocumented Models | Models without a model-level description | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/documentation/#undocumented-models) |
+| Documentation | Undocumented Source | Sources (collections of source tables) without descriptions | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/documentation/#undocumented-sources) |
+| Documentation | Undocumented Source Tables | Source tables without descriptions | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/documentation/#undocumented-source-tables) |
+| Governance | Public Model Missing Contract | Models with public access that do not have a model contract to ensure the data types | [GitHub](https://dbt-labs.github.io/dbt-project-evaluator/0.8/rules/governance/#public-models-without-contracts) |
+
+
+## The Recommendations tab
+
+Models, sources and exposures each also have a Recommendations tab on their resource details page, with the specific recommendations that correspond to that resource:
+
+<Lightbox src="/img/docs/collaborate/dbt-explorer/example-recommendations-tab.png" width="80%" title="Example of the Recommendations tab "/>
+
+

website/docs/docs/dbt-versions/core-upgrade/01-upgrading-to-v1.6.md

@@ -17,7 +17,7 @@ dbt Core v1.6 has three significant areas of focus:
 ## Resources
 
 - [Changelog](https://github.com/dbt-labs/dbt-core/blob/1.6.latest/CHANGELOG.md)
-- [CLI Installation guide](/docs/core/installation-overview
+- [dbt Core installation guide](/docs/core/installation-overview)
 - [Cloud upgrade guide](/docs/dbt-versions/upgrade-core-in-cloud)
 - [Release schedule](https://github.com/dbt-labs/dbt-core/issues/7481)
 

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

@@ -26,7 +26,7 @@ If your dbt job run completed with a status of **Error**, you can rerun it from
 <Lightbox src="/img/docs/deploy/native-retry.gif" width="70%" title="Example of the Rerun options in dbt Cloud"/>
 
 ## Related content
-- [Retry a failed run for a job](/dbt-cloud/api-v2#/operations/Retry%20a%20failed%20run%20for%20a%20job) API endpoint
+- [Retry a failed run for a job](/dbt-cloud/api-v2#/operations/Retry%20Failed%20Job) API endpoint
 - [Run visibility](/docs/deploy/run-visibility)
 - [Jobs](/docs/deploy/jobs)
-- [Job commands](/docs/deploy/job-commands)
+- [Job commands](/docs/deploy/job-commands)

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

@@ -17,6 +17,8 @@ The dbt Semantic Layer offers a seamless integration with Google Sheets through
 - You have a Google account with access to Google Sheets.
 - You can install Google add-ons.
 - You have a dbt Cloud Environment ID and a [service token](/docs/dbt-cloud-apis/service-tokens) to authenticate with from a dbt Cloud account.
+- You must have a dbt Cloud Team or Enterprise [account](https://www.getdbt.com/pricing). Suitable for both Multi-tenant and Single-tenant deployment.
+  - Single-tenant accounts should contact their account representative for necessary setup and enablement.
 
 ## Installing the add-on 
 

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

@@ -26,7 +26,7 @@ MetricFlow, a powerful component of the dbt Semantic Layer, simplifies the creat
 Use this guide to fully experience the power of the universal dbt Semantic Layer. Here are the following steps you'll take:
 
 - [Create a semantic model](#create-a-semantic-model) in dbt Cloud using MetricFlow
-- [Define metrics](#define-metrics) in dbt Cloud using MetricFlow
+- [Define metrics](#define-metrics) in dbt using MetricFlow
 - [Test and query metrics](#test-and-query-metrics) with MetricFlow 
 - [Run a production job](#run-a-production-job) in dbt Cloud
 - [Set up dbt Semantic Layer](#setup) in dbt Cloud 

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

@@ -21,7 +21,8 @@ This integration provides a live connection to the dbt Semantic Layer through Ta
   - Note that Tableau Online does not currently support custom connectors natively. If you use Tableau Online, you will only be able to access the connector in Tableau Desktop.
 - Log in to Tableau Desktop (with Online or Server credentials) or a license to Tableau Server
 - You need your dbt Cloud host, [Environment ID](/docs/use-dbt-semantic-layer/setup-sl#set-up-dbt-semantic-layer) and [service token](/docs/dbt-cloud-apis/service-tokens) to log in. This account should be set up with the dbt Semantic Layer.
-- You must have a dbt Cloud Team or Enterprise [account](https://www.getdbt.com/pricing) and multi-tenant [deployment](/docs/cloud/about-cloud/regions-ip-addresses). (Single-Tenant coming soon)
+- You must have a dbt Cloud Team or Enterprise [account](https://www.getdbt.com/pricing). Suitable for both Multi-tenant and Single-tenant deployment. 
+  - Single-tenant accounts should contact their account representative for necessary setup and enablement.
 
 
 ## Installing the Connector

website/docs/faqs/Tests/testing-sources.md

@@ -9,7 +9,7 @@ id: testing-sources
 To run tests on all sources, use the following command:
 
 ```shell
-$ dbt test --select source:*
+  dbt test --select "source:*"
 ```
 
 (You can also use the `-s` shorthand here instead of `--select`)

website/docs/terms/data-wrangling.md

@@ -51,7 +51,7 @@ The cleaning stage involves using different functions so that the values in your
 - Removing appropriate duplicates or nulls you found in the discovery process
 - Eliminating unnecessary characters or spaces from values
 
-Certain cleaning steps, like removing rows with null values, are helpful to do at the beginning of the process because removing nulls and duplicates from the start can increase the performance of your downstream models.  In the cleaning step, it’s important to follow a standard for your transformations here. This means you should be following a consistent naming convention for your columns (especially for your <Term id="primary-key">primary keys</Term>) and casting to the same timezone and datatypes throughout your models. Examples include making sure all dates are in UTC time rather than source timezone-specific, all string in either lower or upper case, etc. 
+Certain cleaning steps, like removing rows with null values, are helpful to do at the beginning of the process because removing nulls and duplicates from the start can increase the performance of your downstream models.  In the cleaning step, it’s important to follow a standard for your transformations here. This means you should be following a consistent naming convention for your columns (especially for your <Term id="primary-key">primary keys</Term>) and casting to the same timezone and datatypes throughout your models. Examples include making sure all dates are in UTC time rather than source timezone-specific, all strings are in either lower or upper case, etc. 
 
 :::tip dbt to the rescue!
 If you're struggling to do all the cleaning on your own, remember that dbt packages ([dbt expectations](https://github.com/calogica/dbt-expectations), [dbt_utils](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/), and [re_data](https://www.getre.io/)) and their macros are also available to help you clean up your data.

website/sidebars.js

@@ -424,6 +424,8 @@ const sidebarSettings = {
           link: { type: "doc", id: "docs/collaborate/explore-projects" },
           items: [
             "docs/collaborate/explore-projects",
+            "docs/collaborate/model-performance",
+            "docs/collaborate/project-recommendations",
             "docs/collaborate/explore-multiple-projects",
           ],
         },

website/snippets/_cloud-environments-info.md

@@ -62,7 +62,7 @@ By default, all environments will use the default branch in your repository (usu
 For more info, check out this [FAQ page on this topic](/faqs/Environments/custom-branch-settings)!
 
 
-### Extended attributes <Lifecycle status='beta'/>
+### Extended attributes
 
 :::note 
 Extended attributes are retrieved and applied only at runtime when `profiles.yml` is requested for a specific Cloud run. Extended attributes are currently _not_ taken into consideration for Cloud-specific features such as PrivateLink or SSH Tunneling that do not rely on `profiles.yml` values.

website/snippets/_new-sl-setup.md

@@ -1,6 +1,7 @@
 You can set up the dbt Semantic Layer in dbt Cloud at the environment and project level. Before you begin:
 
-- You must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment. Single-tenant coming soon.
+- You must have a dbt Cloud Team or Enterprise account. Suitable for both Multi-tenant and Single-tenant deployment.
+  - Single-tenant accounts should contact their account representative for necessary setup and enablement.
 - You must be part of the Owner group, and have the correct [license](/docs/cloud/manage-access/seats-and-users) and [permissions](/docs/cloud/manage-access/self-service-permissions) to configure the Semantic Layer:
     * Enterprise plan — Developer license with Account Admin permissions. Or Owner with a Developer license, assigned Project Creator, Database Admin, or Admin permissions.
     * Team plan — Owner with a Developer license.