Merge branch 'current' into update-simple-metrics

website/dbt-versions.js

@@ -20,6 +20,7 @@ exports.versions = [
   },
   {
     version: "1.9",
+    EOLDate: "2025-12-08",
   },
   {
     version: "1.8",

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

@@ -184,11 +184,10 @@ Several configurations are relevant to microbatch models, and some are required:
 | Config   |  Description   | Default | Type | Required  |
 |----------|---------------|---------|------|---------|
 | [`event_time`](/reference/resource-configs/event-time)  | The column indicating "at what time did the row occur." Required for your microbatch model and any direct parents that should be filtered.   | N/A     |  Column  |  Required |
-| `begin`      |  The "beginning of time" for the microbatch model. This is the starting point for any initial or full-refresh builds. For example, a daily-grain microbatch model run on `2024-10-01` with `begin = '2023-10-01` will process 366 batches (it's a leap year!) plus the batch for "today."        | N/A     | Date   | Required |
-| `batch_size` |  The granularity of your batches. Supported values are `hour`, `day`, `month`, and `year`    | N/A     | String  | Required |
-| `lookback`   | Process X batches prior to the latest bookmark to capture late-arriving records.    | `1`     | Integer | Optional |
-| `concurrent_batches` | An override for whether batches run concurrently (at the same time) or sequentially (one after the other). | `None` | Boolean | Optional |
-
+| [`begin`](/reference/resource-configs/begin)      |  The "beginning of time" for the microbatch model. This is the starting point for any initial or full-refresh builds. For example, a daily-grain microbatch model run on `2024-10-01` with `begin = '2023-10-01` will process 366 batches (it's a leap year!) plus the batch for "today."        | N/A     | Date   | Required |
+| [`batch_size`](/reference/resource-configs/batch-size) |  The granularity of your batches. Supported values are `hour`, `day`, `month`, and `year`    | N/A     | String  | Required |
+| [`lookback`](/reference/resource-configs/lookback)   | Process X batches prior to the latest bookmark to capture late-arriving records.    | `1`     | Integer | Optional |
+| [`concurrent_batches`](/reference/resource-properties/concurrent_batches) | An override for whether batches run concurrently (at the same time) or sequentially (one after the other). | `None` | Boolean | Optional |
 
 <Lightbox src="/img/docs/building-a-dbt-project/microbatch/event_time.png" title="The event_time column configures the real-world time of this record"/>
 
@@ -290,7 +289,7 @@ The microbatch strategy offers the benefit of updating a model in smaller, more
 
 Parallel batch execution means that multiple batches are processed at the same time, instead of one after the other (sequentially) for faster processing of your microbatch models.  
 
-dbt automatically detects whether a batch can be run in parallel in most cases, which means you don’t need to configure this setting. However, the `concurrent_batches` config is available as an override (not a gate), allowing you to specify whether batches should or shouldn’t be run in parallel in specific cases.
+dbt automatically detects whether a batch can be run in parallel in most cases, which means you don’t need to configure this setting. However, the [`concurrent_batches` config](/reference/resource-properties/concurrent_batches) is available as an override (not a gate), allowing you to specify whether batches should or shouldn’t be run in parallel in specific cases.
 
 For example, if you have a microbatch model with 12 batches, you can execute those batches to run in parallel. Specifically they'll run in parallel limited by the number of [available threads](/docs/running-a-dbt-project/using-threads).
 

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

@@ -33,7 +33,7 @@ Click the name of the adapter in the below table for more information about supp
 | [dbt-databricks](/reference/resource-configs/databricks-configs#incremental-models)                 |     ✅    |    ✅   |                 |          ✅         |          ✅         |
 | [dbt-snowflake](/reference/resource-configs/snowflake-configs#merge-behavior-incremental-models)    |     ✅    |    ✅   |        ✅        |                    |      ✅            |
 | [dbt-trino](/reference/resource-configs/trino-configs#incremental)                                  |     ✅    |    ✅   |        ✅        |                    |                    |
-| [dbt-fabric](/reference/resource-configs/fabric-configs#incremental)                                |     ✅    |    ✅   |        ✅          |                    |                    |
+| [dbt-fabric](/reference/resource-configs/fabric-configs#incremental)                                |     ✅    |         |        ✅          |                    |                    |
 | [dbt-athena](/reference/resource-configs/athena-configs#incremental-models)                         |     ✅    |    ✅   |                 |          ✅         |                    |
 
 ### Configuring incremental strategy

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

@@ -61,8 +61,14 @@ To set up [personal access tokens (PATs)](https://help.tableau.com/current/serve
    <Lightbox src="/img/docs/cloud-integrations/auto-exposures/cloud-integration-details.jpg" title="Enter the details for the exposure connection."/>
 4. Select the collections you want to include for auto exposures. 
 
-   dbt Cloud automatically imports and syncs any workbook within the selected collections. New additions to the collections will be added to the lineage in dbt Cloud during the next automatic sync (usually once per day).
    <Lightbox src="/img/docs/cloud-integrations/auto-exposures/cloud-select-collections.jpg" title="Select the collections you want to include for auto exposures."/>
+
+      :::info
+      dbt Cloud automatically imports and syncs any workbook within the selected collections. New additions to the collections will be added to the lineage in dbt Cloud during the next sync (automatically once per day).
+
+      dbt Cloud immediately starts a sync when you update the selected collections list, capturing new workbooks and removing irrelevant ones.
+      :::
+
 5. Click **Save**. 
 
 dbt Cloud imports everything in the collection(s) and you can continue to view them in Explorer. For more information on how to view and use auto-exposures, refer to [View auto-exposures from dbt Explorer](/docs/collaborate/auto-exposures) page.

website/docs/docs/cloud/cloud-cli-installation.md

@@ -319,3 +319,10 @@ This alias will allow you to use the <code>dbt-cloud</code> command to invoke th
 If you've ran a dbt command and receive a <code>Session occupied</code> error, you can reattach to your existing session with <code>dbt reattach</code> and then press <code>Control-C</code> and choose to cancel the invocation.
 
 </DetailsToggle>
+
+<DetailsToggle alt_header="Why am I receiving a `Stuck session` error when trying to run a new command?">
+
+
+The Cloud CLI allows only one command that writes to the data warehouse at a time. If you attempt to run multiple write commands simultaneously (for example, `dbt run` and `dbt build`), you will encounter a `stuck session` error. To resolve this, cancel the specific invocation by passing its ID to the cancel command. For more information, refer to [parallel execution](/reference/dbt-commands#parallel-execution).
+
+</DetailsToggle>

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

@@ -34,7 +34,7 @@ The following steps will walk you through the setup of a Databricks AWS PrivateL
 1. Once dbt Cloud support has notified you that setup is complete, [register the VPC endpoint in Databricks](https://docs.databricks.com/administration-guide/cloud-configurations/aws/privatelink.html#step-3-register-privatelink-objects-and-attach-them-to-a-workspace) and attach it to the workspace:
     - [Register your VPC endpoint](https://docs.databricks.com/en/security/network/classic/vpc-endpoints.html) — Register the VPC endpoint using the VPC endpoint ID provided by dbt Support.
     - [Create a Private Access Settings object](https://docs.databricks.com/en/security/network/classic/private-access-settings.html) — Create a Private Access Settings (PAS) object with your desired public access settings, and setting Private Access Level to **Endpoint**. Choose the registered endpoint created in the previous step.
-    - [Create or update your workspace](https://docs.databricks.com/en/security/network/classic/privatelink.html#step-3d-create-or-update-the-workspace-front-end-back-end-or-both) — Create a workspace, or update your an existing workspace. Under **Advanced configurations → Private Link** choose the private access settings object created in the previous step.
+    - [Create or update your workspace](https://docs.databricks.com/en/security/network/classic/privatelink.html#step-3d-create-or-update-the-workspace-front-end-back-end-or-both) — Create a workspace, or update an existing workspace. Under **Advanced configurations → Private Link** choose the private access settings object created in the previous step.
 
     :::warning
     If using an existing Databricks workspace, all workloads running in the workspace need to be stopped to enable Private Link. Workloads also can't be started for another 20 minutes after making changes. From the [Databricks documentation](https://docs.databricks.com/en/security/network/classic/privatelink.html#step-3d-create-or-update-the-workspace-front-end-back-end-or-both):

website/docs/docs/dbt-versions/core-upgrade/06-upgrading-to-v1.9.md

@@ -49,7 +49,7 @@ Starting in Core 1.9, you can use the new [microbatch strategy](/docs/build/incr
 - Simplified query design: Write your model query for a single batch of data. dbt will use your `event_time`, `lookback`, and `batch_size` configurations to automatically generate the necessary filters for you, making the process more streamlined and reducing the need for you to manage these details.
 - Independent batch processing: dbt automatically breaks down the data to load into smaller batches based on the specified `batch_size` and processes each batch independently, improving efficiency and reducing the risk of query timeouts. If some of your batches fail, you can use `dbt retry` to load only the failed batches.
 - Targeted reprocessing: To load a *specific* batch or batches, you can use the CLI arguments `--event-time-start` and `--event-time-end`.
-- [Automatic parallel batch execution](/docs/build/incremental-microbatch#parallel-batch-execution): Process multiple batches at the same time, instead of one after the other (sequentially) for faster processing of your microbatch models. dbt intelligently auto-detects if your batches can run in parallel, while also allowing you to manually override parallel execution with the `concurrent_batches` config.
+- [Automatic parallel batch execution](/docs/build/incremental-microbatch#parallel-batch-execution): Process multiple batches at the same time, instead of one after the other (sequentially) for faster processing of your microbatch models. dbt intelligently auto-detects if your batches can run in parallel, while also allowing you to manually override parallel execution with the [`concurrent_batches` config](/reference/resource-properties/concurrent_batches).
 
 
 Currently microbatch is supported on these adapters with more to come:

website/docs/docs/dbt-versions/release-notes.md

@@ -20,9 +20,11 @@ Release notes are grouped by month for both multi-tenant and virtual private clo
 
 ## December 2024
 
+- **New**: [Model notifications](/docs/deploy/model-notifications) are now generally available in dbt Cloud. These notifications alert model owners through email about any issues encountered by models and tests as soon as they occur while running a job.
 - **New**: You can now use your [Azure OpenAI key](/docs/cloud/account-integrations?ai-integration=azure#ai-integrations) (available in beta) to use dbt Cloud features like [dbt Copilot](/docs/cloud/dbt-copilot) and [Ask dbt](/docs/cloud-integrations/snowflake-native-app) . Additionally, you can use your own [OpenAI API key](/docs/cloud/account-integrations?ai-integration=openai#ai-integrations) or use [dbt Labs-managed OpenAI](/docs/cloud/account-integrations?ai-integration=dbtlabs#ai-integrations) key. Refer to [AI integrations](/docs/cloud/account-integrations#ai-integrations) for more information.
 - **New**: The [`hard_deletes`](/reference/resource-configs/hard-deletes) config gives you more control on how to handle deleted rows from the source. Supported options are `ignore` (default), `invalidate` (replaces the legacy `invalidate_hard_deletes=true`), and `new_record`. Note that `new_record` will create a new metadata column in the snapshot table.
 
+
 ## November 2024
 - **Enhancement**: Trust signal icons in dbt Explorer are now available for Exposures, providing a quick view of data health while browsing resources. To view trust signal icons, go to dbt Explorer and click **Exposures** under the **Resource** tab. Refer to [Trust signal for resources](/docs/collaborate/explore-projects#trust-signals-for-resources) for more info.
 - **Bug**: Identified and fixed an error with Semantic Layer queries that take longer than 10 minutes to complete.

website/docs/docs/deploy/deploy-environments.md

@@ -35,7 +35,7 @@ To create a new dbt Cloud deployment environment, navigate to **Deploy** -> **En
 
 In dbt Cloud, each project can have one designated deployment environment, which serves as its production environment. This production environment is _essential_ for using features like dbt Explorer and cross-project references. It acts as the source of truth for the project's production state in dbt Cloud.
 
-<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/prod-settings.jpg" width="70%" title="Set your production environment as the default environment in your Environment Settings"/>
+<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/prod-settings-1.png" width="100%" title="Set your production environment as the default environment in your Environment Settings"/>
 
 ### Semantic Layer
 

website/docs/docs/deploy/model-notifications.md

@@ -3,37 +3,31 @@ title: "Model notifications"
 description: "While a job is running, receive email notifications in real time about any issues with your models and tests. "
 ---
 
-# Model notifications <Lifecycle status="beta" />
-
 Set up dbt to notify the appropriate model owners through email about issues as soon as they occur, while the job is still running. Model owners can specify which statuses to receive notifications about: 
 
 - `Success` and `Fails` for models
 - `Warning`, `Success`, and `Fails` for tests
 
 With model-level notifications, model owners can be the first ones to know about issues before anyone else (like the stakeholders). 
 
-:::info Beta feature
-
-This feature is currently available in [beta](/docs/dbt-versions/product-lifecycles#dbt-cloud) to a limited group of users and is gradually being rolled out. If you're in the beta, please contact the Support team at [email protected] for assistance or questions.
-
-:::
-
 To be timely and keep the number of notifications to a reasonable amount when multiple models or tests trigger them, dbt observes the following guidelines when notifying the owners:  
 
 - Send a notification to each unique owner/email during a job run about any models (with status of failure/success) or tests (with status of warning/failure/success). Each owner receives only one notification, the initial one.
-- Don't send any notifications about subsequent models or tests while a dbt job is still running.
-- At the end of a job run, each owner receives a notification, for each of the statuses they specified to be notified about, with a list of models and tests that have that status. 
+- No notifications sent about subsequent models or tests while a dbt job is still running.
+- Each owner/user who subscribes to notifications for one or more statuses (like failure, success, warning) will receive only _one_ email notification at the end of the job run.
+- The email includes a consolidated list of all models or tests that match the statuses the user subscribed to, instead of sending separate emails for each status.
 
 Create configuration YAML files in your project for dbt to send notifications about the status of your models and tests.
 
 ## Prerequisites
 - Your dbt Cloud administrator has [enabled the appropriate account setting](#enable-access-to-model-notifications) for you.
 - Your environment(s) must be on a [release track](/docs/dbt-versions/cloud-release-tracks) instead of a legacy dbt Core version.
 
-
 ## Configure groups
 
-Define your groups in any .yml file in your [models directory](/reference/project-configs/model-paths). For example: 
+Define your groups in any `.yml` file in your [models directory](/reference/project-configs/model-paths). Each group must have a single email address specified &mdash; multiple email fields or lists aren't supported.
+
+The following example shows how to define groups in a `groups.yml` file.
 
 <File name='models/groups.yml'>
 
@@ -116,6 +110,6 @@ Provide dbt Cloud account members the ability to configure and receive alerts ab
 To use model-level notifications, your dbt Cloud account must have access to the feature. Ask your dbt Cloud administrator to enable this feature for account members by following these steps:
 
 1. Navigate to **Notification settings** from your profile name in the sidebar (lower left-hand side). 
-1. From **Email notications**, enable the setting **Enable group/owner notifications on models** under the **Model notifications** section. Then, specify which statuses to receive notifications about (Success, Warning, and/or Fails). 
+1. From **Email notifications**, enable the setting **Enable group/owner notifications on models** under the **Model notifications** section. Then, specify which statuses to receive notifications about (Success, Warning, and/or Fails). 
 
   <Lightbox src="/img/docs/dbt-cloud/example-enable-model-notifications.png" title="Example of the setting Enable group/owner notifications on models" /> 

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

@@ -13,7 +13,7 @@ This portion of our documentation will go over dbt Cloud's various capabilities
 - [Run visibility](/docs/deploy/run-visibility) — View your run history to help identify where improvements can be made to scheduled jobs.
 - [Retry jobs](/docs/deploy/retry-jobs) — Rerun your errored jobs from start or the failure point.
 - [Job notifications](/docs/deploy/job-notifications) — Receive email or Slack notifications when a job run succeeds, encounters warnings, fails, or is canceled. 
-- [Model notifications](/docs/deploy/model-notifications)<Lifecycle status="beta"/> &mdash; Receive email notifications about any issues encountered by your models and tests as soon as they occur while running a job. 
+- [Model notifications](/docs/deploy/model-notifications) &mdash; Receive email notifications about any issues encountered by your models and tests as soon as they occur while running a job. 
 - [Webhooks](/docs/deploy/webhooks) &mdash; Use webhooks to send events about your dbt jobs' statuses to other systems.
 - [Leverage artifacts](/docs/deploy/artifacts) &mdash; dbt Cloud generates and saves artifacts for your project, which it uses to power features like creating docs for your project and reporting freshness of your sources.
 - [Source freshness](/docs/deploy/source-freshness) &mdash; Monitor data governance by enabling snapshots to capture the freshness of your data sources. 

website/docs/reference/global-configs/indirect-selection.md

@@ -6,7 +6,7 @@ sidebar: "Indirect selection"
 
 import IndirSelect from '/snippets/_indirect-selection-definitions.md';
 
-Use the `--indirect_selection` flag to `dbt test` or `dbt build` to configure which tests to run for the nodes you specify. You can set this as a CLI flag or an environment variable. In dbt Core, you can also configure user configurations in [YAML selectors](/reference/node-selection/yaml-selectors) or in the `flags:` block of `dbt_project.yml`, which sets project-level flags.
+Use the `--indirect-selection` flag to `dbt test` or `dbt build` to configure which tests to run for the nodes you specify. You can set this as a CLI flag or an environment variable. In dbt Core, you can also configure user configurations in [YAML selectors](/reference/node-selection/yaml-selectors) or in the `flags:` block of `dbt_project.yml`, which sets project-level flags.
 
 When all flags are set, the order of precedence is as follows. Refer to [About global configs](/reference/global-configs/about-global-configs) for more details: