Merge branch 'current' into asmclean/ENTERPRISE-1771-saml-user-ids

.github/workflows/vale.yml

@@ -29,11 +29,11 @@ jobs:
           python-version: '3.x'
 
       - name: Install Vale
-        run: pip install vale==2.27.0  # Install a stable version of Vale
+        run: pip install vale==3.9.1.0  # Install a stable version of Vale
 
       - name: Get changed files
         id: changed-files
-        uses: tj-actions/changed-files@v34
+        uses: tj-actions/changed-files@v45
         with:
           files: |
             website/**/*.md
@@ -63,10 +63,9 @@ jobs:
         uses: errata-ai/vale-action@reviewdog
         with:
           token: ${{ secrets.GITHUB_TOKEN }}
-          reporter: github-check
+          reporter: github-pr-review
           files: ${{ steps.changed-files.outputs.all_changed_and_modified_files }}
           separator: ' '
-          version: '2.27.0'
 
 #      - name: Post summary comment
 #        if: ${{ steps.changed-files.outputs.any_changed == 'true' }}

styles/Vocab/EN/accept.txt → styles/config/vocabularies/EN/accept.txt

@@ -65,3 +65,5 @@ N/A
 pseudocolumn
 yml
 values=
+dbt v\\d+\\.\\d+
+v\\d+\\.\\d+

styles/custom/Typos.yml

@@ -37,3 +37,5 @@ tokens:
   - '\w+/\w+|\w+-\w+|n/a'
   - 'n/a' 
   - 'N/A'
+  - 'dbt v\\d+\\.\\d+'
+  - 'v\\d+\\.\\d+ '

website/blog/2022-04-14-add-ci-cd-to-bitbucket.md

@@ -197,7 +197,7 @@ Reading the file over, you can see that we:
 
 In summary, anytime anything is pushed to main, we’ll ensure our production database reflects the dbt transformation, and we’ve saved the resulting artifacts to defer to.
 
-> ❓ **What are artifacts and why should I defer to them?** dbt artifacts are metadata of the last run - what models and tests were defined, which ones ran successfully, and which failed. If a future dbt run is set to ***defer*** to this metadata, it means that it can select models and tests to run based on their state, including and especially their difference from the reference metadata. See [Artifacts](https://docs.getdbt.com/reference/artifacts/dbt-artifacts), [Selection methods: “state”](https://docs.getdbt.com/reference/node-selection/methods#the-state-method), and [Caveats to state comparison](https://docs.getdbt.com/reference/node-selection/state-comparison-caveats) for details.
+> ❓ **What are artifacts and why should I defer to them?** dbt artifacts are metadata of the last run - what models and tests were defined, which ones ran successfully, and which failed. If a future dbt run is set to ***defer*** to this metadata, it means that it can select models and tests to run based on their state, including and especially their difference from the reference metadata. See [Artifacts](https://docs.getdbt.com/reference/artifacts/dbt-artifacts), [Selection methods: “state”](https://docs.getdbt.com/reference/node-selection/methods#state), and [Caveats to state comparison](https://docs.getdbt.com/reference/node-selection/state-comparison-caveats) for details.
 
 ### Slim Continuous Integration: Retrieve the artifacts and do a state-based run
 

website/dbt-versions.js

@@ -20,6 +20,7 @@ exports.versions = [
   },
   {
     version: "1.9",
+    customDisplay: "1.9 (Compatible)",
     EOLDate: "2025-12-08",
   },
   {

website/docs/best-practices/how-we-style/5-how-we-style-our-yaml.md

@@ -7,6 +7,7 @@ id: 5-how-we-style-our-yaml
 
 - 2️⃣ Indents should be two spaces
 - ➡️ List items should be indented
+- 🔠 List items with a single entry can be a string. For example, `'select': 'other_user'`, but it's best practice to provide the argument as an explicit list. For example, `'select': ['other_user']`
 - 🆕 Use a new line to separate list items that are dictionaries where appropriate
 - 📏 Lines of YAML should be no longer than 80 characters.
 - 🛠️ Use the [dbt JSON schema](https://github.com/dbt-labs/dbt-jsonschema) with any compatible IDE and a YAML formatter (we recommend [Prettier](https://prettier.io/)) to validate your YAML files and format them automatically.

website/docs/docs/build/data-tests.md

@@ -9,6 +9,11 @@ id: "data-tests"
 keywords:
   - test, tests, testing, dag
 ---
+
+import CopilotBeta from '/snippets/_dbt-copilot-avail.md';
+
+<CopilotBeta resource='data tests' />
+
 ## Related reference docs
 * [Test command](/reference/commands/test)
 * [Data test properties](/reference/resource-properties/data-tests)

website/docs/docs/build/dbt-tips.md

@@ -40,7 +40,7 @@ Leverage these dbt packages to streamline your workflow:
 - Set `vars` in your `dbt_project.yml` to define global defaults for certain conditions, which you can then override using the `--vars` flag in your commands.
 - Use [for loops](/guides/using-jinja?step=3) in Jinja to <Term id="dry">DRY</Term> up repetitive logic, such as selecting a series of columns that all require the same transformations and naming patterns to be applied.
 - Instead of relying on post-hooks, use the [grants config](/reference/resource-configs/grants) to apply permission grants in the warehouse resiliently.
-- Define [source-freshness](/docs/build/sources#snapshotting-source-data-freshness) thresholds on your sources to avoid running transformations on data that has already been processed.
+- Define [source-freshness](/docs/build/sources#source-data-freshness) thresholds on your sources to avoid running transformations on data that has already been processed.
 - Use the `+` operator on the left of a model `dbt build --select +model_name` to run a model and all of its upstream dependencies. Use the `+` operator on the right of the model `dbt build --select model_name+` to run a model and everything downstream that depends on it.
 - Use `dir_name` to run all models in a package or directory.
 - Use the `@` operator on the left of a model in a non-state-aware CI setup to test it. This operator runs all of a selection’s parents and children, and also runs the parents of its children, which in a fresh CI schema will likely not exist yet.

website/docs/docs/build/documentation.md

@@ -7,6 +7,10 @@ id: "documentation"
 Good documentation for your dbt models will help downstream consumers discover and understand the datasets you curate for them.
 dbt provides a way to generate documentation for your dbt project and render it as a website.
 
+import CopilotBeta from '/snippets/_dbt-copilot-avail.md';
+
+<CopilotBeta resource='documentation' />
+
 ## Related documentation
 
 * [Declaring properties](/reference/configs-and-properties)

website/docs/docs/build/exposures.md

@@ -77,5 +77,5 @@ When we generate the [dbt Explorer site](/docs/collaborate/explore-projects), yo
 ## Related docs
 
 * [Exposure properties](/reference/exposure-properties)
-* [`exposure:` selection method](/reference/node-selection/methods#the-exposure-method)
+* [`exposure:` selection method](/reference/node-selection/methods#exposure)
 * [Data health tiles](/docs/collaborate/data-tile)

website/docs/docs/build/groups.md

@@ -119,4 +119,4 @@ dbt.exceptions.DbtReferenceError: Parsing Error
 
 * [Model Access](/docs/collaborate/govern/model-access#groups)
 * [Group configuration](/reference/resource-configs/group)
-* [Group selection](/reference/node-selection/methods#the-group-method)
+* [Group selection](/reference/node-selection/methods#group)

website/docs/docs/build/materializations.md

@@ -18,7 +18,11 @@ You can also configure [custom materializations](/guides/create-new-materializat
 
 
 ## Configuring materializations
-By default, dbt models are materialized as "views". Models can be configured with a different materialization by supplying the `materialized` configuration parameter as shown below.
+By default, dbt models are materialized as "views". Models can be configured with a different materialization by supplying the [`materialized` configuration](/reference/resource-configs/materialized) parameter as shown in the following tabs.
+
+<Tabs>
+
+<TabItem value="Project file">
 
 <File name='dbt_project.yml'>
 
@@ -49,6 +53,10 @@ models:
 
 </File>
 
+</TabItem>
+
+<TabItem value="Model file">
+
 Alternatively, materializations can be configured directly inside of the model sql files. This can be useful if you are also setting [Performance Optimization] configs for specific models (for example, [Redshift specific configurations](/reference/resource-configs/redshift-configs) or [BigQuery specific configurations](/reference/resource-configs/bigquery-configs)).
 
 <File name='models/events/stg_event_log.sql'>
@@ -63,6 +71,29 @@ from ...
 
 </File>
 
+</TabItem>
+
+<TabItem value="Property file">
+
+Materializations can also be configured in the model's `properties.yml` file.  The following example shows the `table` materialization type. For a complete list of materialization types, refer to [materializations](/docs/build/materializations#materializations).
+
+<File name='models/properties.yml'>
+
+```yaml
+version: 2
+
+models:
+  - name: events
+    config:
+      materialized: table
+```
+
+</File>
+
+</TabItem>
+
+</Tabs>
+
 ## Materializations
 
 

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

@@ -271,9 +271,9 @@ metrics:
       measure:
         name: cancellations_usd  # Specify the measure you are creating a proxy for.
         fill_nulls_with: 0
+        join_to_timespine: true
     filter: |
       {{ Dimension('order__value')}} > 100 and {{Dimension('user__acquisition')}} is not null
-    join_to_timespine: true
 ```
 </File>
 

website/docs/docs/build/packages.md

@@ -157,9 +157,57 @@ packages:
 
 Where `name: 'dbt_utils'` specifies the subfolder of `dbt_packages` that's created for the package source code to be installed within.
 
-### Private packages
+## Private packages
 
-#### SSH Key Method (Command Line only)
+### Native private packages <Lifecycle status='beta'/> 
+
+dbt Cloud supports private packages from [supported](#prerequisites) Git repos leveraging an exisiting [configuration](/docs/cloud/git/git-configuration-in-dbt-cloud) in your environment. Previously, you had to configure a [token](#git-token-method) to retrieve packages from your private repos.  
+
+#### Prerequisites
+
+To use native private packages, you must have one of the following Git providers configured in the **Integrations** section of your **Account settings**:
+- [GitHub](/docs/cloud/git/connect-github)
+- [Azure DevOps](/docs/cloud/git/connect-azure-devops)
+- Support for GitLab is coming soon.
+
+
+#### Configuration
+
+Use the `private` key in your `packages.yml` or `dependencies.yml` to clone package repos using your existing dbt Cloud Git integration without having to provision an access token or create a dbt Cloud environment variable:
+
+<File name="packages.yml">
+
+```yaml
+packages:
+  - private: dbt-labs/awesome_repo
+  - package: normal packages
+
+	[...]
+```
+
+</File>
+
+You can pin private packages similar to regular dbt packages:
+
+```yaml
+packages:
+  - private: dbt-labs/awesome_repo
+    revision: "0.9.5" # Pin to a tag, branch, or complete 40-character commit hash
+  
+```
+
+If you are using multiple Git integrations, disambiguate by adding the provider key:
+
+```yaml
+packages:
+  - private: dbt-labs/awesome_repo
+    provider: "github" # GitHub and Azure are currently supported. GitLab is coming soon.
+
+```
+
+With this method, you can retrieve private packages from an integrated Git provider without any additional steps to connect. 
+
+### SSH key method (command line only)
 If you're using the Command Line, private packages can be cloned via SSH and an SSH key.
 
 When you use SSH keys to authenticate to your git remote server, you don’t need to supply your username and password each time. Read more about SSH keys, how to generate them, and how to add them to your git provider here: [Github](https://docs.github.com/en/github/authenticating-to-github/connecting-to-github-with-ssh) and [GitLab](https://docs.gitlab.com/ee/user/ssh.html).
@@ -177,7 +225,14 @@ packages:
 If you're using dbt Cloud, the SSH key method will not work, but you can use the [HTTPS Git Token Method](https://docs.getdbt.com/docs/build/packages#git-token-method).
 
 
-#### Git token method
+### Git token method
+
+:::note
+
+dbt Cloud has [native support](#native-private-packages) for Git hosted private packages with GitHub and Azure DevOps (GitLab coming soon). If you are using a supported [integrated Git environment](/docs/cloud/git/git-configuration-in-dbt-cloud), you no longer need to configure Git tokens to retrieve private packages. 
+
+:::
+
 This method allows the user to clone via HTTPS by passing in a git token via an environment variable. Be careful of the expiration date of any token you use, as an expired token could cause a scheduled run to fail. Additionally, user tokens can create a challenge if the user ever loses access to a specific repo.
 
 
@@ -246,7 +301,7 @@ Read more about creating a Personal Access Token [here](https://confluence.atlas
 
 
 
-#### Configure subdirectory for packaged projects
+## Configure subdirectory for packaged projects
 
 In general, dbt expects `dbt_project.yml` to be located as a top-level file in a package. If the packaged project is instead nested in a subdirectory—perhaps within a much larger mono repo—you can optionally specify the folder path as `subdirectory`. dbt will attempt a [sparse checkout](https://git-scm.com/docs/git-sparse-checkout) of just the files located within that subdirectory. Note that you must be using a recent version of `git` (`>=2.26.0`).
 

website/docs/docs/build/projects.md

@@ -22,6 +22,8 @@ At a minimum, all a project needs is the `dbt_project.yml` project configuration
 | [metrics](/docs/build/build-metrics-intro) | A way for you to define metrics for your project. |
 | [groups](/docs/build/groups) | Groups enable collaborative node organization in restricted collections. |
 | [analysis](/docs/build/analyses) | A way to organize analytical SQL queries in your project such as the general ledger from your QuickBooks. |
+| [semantic models](/docs/build/semantic-models) | Semantic models define the foundational data relationships in [MetricFlow](/docs/build/about-metricflow) and the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), enabling you to query metrics using a semantic graph. |
+| [saved queries](/docs/build/saved-queries) | Saved queries organize reusable queries by grouping metrics, dimensions, and filters into nodes visible in the dbt DAG. |
 
 When building out the structure of your project, you should consider these impacts on your organization's workflow:
 

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

@@ -9,6 +9,10 @@ tags: [Metrics, Semantic Layer]
 pagination_next: "docs/build/dimensions"
 ---
 
+import CopilotBeta from '/snippets/_dbt-copilot-avail.md';
+
+<CopilotBeta resource='semantic models' />
+
 Semantic models are the foundation for data definition in MetricFlow, which powers the dbt Semantic Layer:
 
 - Think of semantic models as nodes connected by entities in a semantic graph.

website/docs/docs/build/sources.md

@@ -130,11 +130,11 @@ You can find more details on the available properties for sources in the [refere
 <FAQ path="Tests/testing-sources" />
 <FAQ path="Runs/running-models-downstream-of-source" />
 
-## Snapshotting source data freshness
-With a couple of extra configs, dbt can optionally snapshot the "freshness" of the data in your source tables. This is useful for understanding if your data pipelines are in a healthy state, and is a critical component of defining SLAs for your warehouse.
+## Source data freshness
+With a couple of extra configs, dbt can optionally capture the "freshness" of the data in your source tables. This is useful for understanding if your data pipelines are in a healthy state, and is a critical component of defining SLAs for your warehouse.
 
 ### Declaring source freshness
-To configure sources to snapshot freshness information, add a `freshness` block to your source and `loaded_at_field` to your table declaration:
+To configure source freshness information, add a `freshness` block to your source and `loaded_at_field` to your table declaration:
 
 <File name='models/<filename>.yml'>
 
@@ -164,14 +164,14 @@ sources:
 
 </File>
 
-In the `freshness` block, one or both of `warn_after` and `error_after` can be provided. If neither is provided, then dbt will not calculate freshness snapshots for the tables in this source.
+In the `freshness` block, one or both of `warn_after` and `error_after` can be provided. If neither is provided, then dbt will not calculate freshness for the tables in this source.
 
 Additionally, the `loaded_at_field` is required to calculate freshness for a table. If a `loaded_at_field` is not provided, then dbt will not calculate freshness for the table.
 
 These configs are applied hierarchically, so `freshness` and `loaded_at_field` values specified for a `source` will flow through to all of the `tables` defined in that source. This is useful when all of the tables in a source have the same `loaded_at_field`, as the config can just be specified once in the top-level source definition.
 
 ### Checking source freshness
-To snapshot freshness information for your sources, use the `dbt source freshness` command ([reference docs](/reference/commands/source)):
+To obtain freshness information for your sources, use the `dbt source freshness` command ([reference docs](/reference/commands/source)):
 
 ```
 $ dbt source freshness
@@ -182,7 +182,7 @@ Behind the scenes, dbt uses the freshness properties to construct a `select` que
 ```sql
 select
   max(_etl_loaded_at) as max_loaded_at,
-  convert_timezone('UTC', current_timestamp()) as snapshotted_at
+  convert_timezone('UTC', current_timestamp()) as calculated_at
 from raw.jaffle_shop.orders
 
 ```
@@ -198,7 +198,7 @@ Some databases can have tables where a filter over certain columns are required,
 ```sql
 select
   max(_etl_loaded_at) as max_loaded_at,
-  convert_timezone('UTC', current_timestamp()) as snapshotted_at
+  convert_timezone('UTC', current_timestamp()) as calculated_at
 from raw.jaffle_shop.orders
 where _etl_loaded_at >= date_sub(current_date(), interval 1 day)
 ```

website/docs/docs/cloud-integrations/configure-auto-exposures.md

@@ -6,7 +6,7 @@ description: "Import and auto-generate exposures from dashboards and understand
 image: /img/docs/cloud-integrations/auto-exposures/explorer-lineage2.jpg
 ---
 
-# Configure auto-exposures <Lifecycle status="preview,enterprise" />
+# Configure auto-exposures <Lifecycle status="enterprise" />
 
 As a data team, it’s critical that you have context into the downstream use cases and users of your data products. [Auto-exposures](/docs/collaborate/auto-exposures) integrates natively with Tableau and [auto-generates downstream lineage](/docs/collaborate/auto-exposures#view-auto-exposures-in-dbt-explorer) in dbt Explorer for a richer experience.
 

website/docs/docs/cloud-integrations/overview.md

@@ -13,7 +13,7 @@ Many data applications integrate with dbt Cloud, enabling you to leverage the po
 <div className="grid--3-col">
 
 <Card
-    title="Configure auto-exposures (preview)"
+    title="Configure auto-exposures"
     body="Import and auto-generate exposures from dashboards to understand how models are used in downstream tools for a richer downstream lineage."
     link="/docs/cloud-integrations/configure-auto-exposures"
     icon="dbt-bit"/>

website/docs/docs/cloud/about-cloud-develop-defer.md

@@ -19,7 +19,7 @@ When using `--defer`, dbt Cloud will follow this order of execution for resolvin
 2. If a development version doesn't exist, dbt uses the staging locations of parent relations based on metadata from the staging environment.
 3. If both a development and staging version doesn't exist, dbt uses the production locations of parent relations based on metadata from the production environment.
 
-**Note:** Passing the `--favor-state` flag will always resolve refs using production metadata, regardless of the presence of a development relation, skipping step #1.
+**Note:** Passing the `--favor-state` flag will always resolve refs using staging metadata if available; otherwise, it defaults to production metadata regardless of the presence of a development relation, skipping step #1.
 
 For a clean slate, it's a good practice to drop the development schema at the start and end of your development cycle.
 

website/docs/docs/cloud/connect-data-platform/about-connections.md

@@ -8,7 +8,7 @@ pagination_prev: null
 ---
 dbt Cloud can connect with a variety of data platform providers including: 
 - [AlloyDB](/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb) 
-- [Amazon Athena](/docs/cloud/connect-data-platform/connect-amazon-athena)<Lifecycle status="Preview" />
+- [Amazon Athena](/docs/cloud/connect-data-platform/connect-amazon-athena)
 - [Amazon Redshift](/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb) 
 - [Apache Spark](/docs/cloud/connect-data-platform/connect-apache-spark)
 - [Azure Synapse Analytics](/docs/cloud/connect-data-platform/connect-azure-synapse-analytics)