Merge branch 'current' into dbeatty10-patch-1

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

@@ -34,6 +34,7 @@ Note that we use the double colon (::) to indicate whether a parameter is nested
 | `base_measure:name` | The base conversion event measure. |  Required | String |
 | `base_measure:fill_nulls_with` | Set the value in your metric definition instead of null (such as zero). | Optional | String |
 | `base_measure:join_to_timespine` | Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. | Optional | Boolean |
+| `base_measure:filter` | Optional `filter` used to apply to the base measure. | Optional | String |
 | `conversion_measure` | A list of conversion measure inputs. | Required | Dict |
 | `conversion_measure:name` | The base conversion event measure.| Required | String |
 | `conversion_measure:fill_nulls_with` | Set the value in your metric definition instead of null (such as zero). | Optional | String |
@@ -61,6 +62,7 @@ metrics:
           name: The name of the measure # Required
           fill_nulls_with: Set the value in your metric definition instead of null (such as zero) # Optional
           join_to_timespine: true/false # Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. # Optional
+          filter: The filter used to apply to the base measure. # Optional
         conversion_measure:
           name: The name of the measure # Required
           fill_nulls_with: Set the value in your metric definition instead of null (such as zero) # Optional
@@ -105,12 +107,13 @@ Next, define a conversion metric as follows:
 - name: visit_to_buy_conversion_rate_7d
   description: "Conversion rate from visiting to transaction in 7 days"
   type: conversion
-  label: Visit to Buy Conversion Rate (7-day window)
+  label: Visit to buy conversion rate (7-day window)
   type_params:
     conversion_type_params:
       base_measure:
         name: visits
         fill_nulls_with: 0
+        filter: {{ Dimension('visits__referrer_id') }} = 'facebook'
       conversion_measure:
         name: sellers
       entity: user

website/docs/docs/build/simple.md

@@ -24,6 +24,8 @@ Note that we use the double colon (::) to indicate whether a parameter is nested
 | `type_params` | The type parameters of the metric. | Required | Dict |
 | `measure` | A list of measure inputs. | Required | List |
 | `measure:name` | The measure you're referencing. | Required | String |
+| `measure:alias` | Optional [`alias`](/reference/resource-configs/alias) to rename the measure. | Optional | String |
+| `measure:filter` | Optional `filter` applied to the measure. | Optional | String |
 | `measure:fill_nulls_with` | Set the value in your metric definition instead of null (such as zero). | Optional | String |
 | `measure:join_to_timespine` | Indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. Default `false`. | Optional | Boolean |
 
@@ -38,6 +40,8 @@ metrics:
     type_params: # Required
       measure: 
         name: The name of your measure # Required
+        alias: The alias applied to the measure. # Optional
+        filter: The filter applied to the measure. # Optional
         fill_nulls_with: Set value instead of null  (such as zero) # Optional
         join_to_timespine: true/false # Boolean that indicates if the aggregated measure should be joined to the time spine table to fill in missing dates. # Optional
 
@@ -65,9 +69,11 @@ If you've already defined the measure using the `create_metric: true` parameter,
           name: customers # The measure you are creating a proxy of.
           fill_nulls_with: 0 
           join_to_timespine: true
+          alias: customer_count
+          filter: {{ Dimension('customer__customer_total') }} >= 20
     - name: large_orders
       description: "Order with order values over 20."
-      type: SIMPLE
+      type: simple
       label: Large orders
       type_params:
         measure: 

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/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/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: