Merge branch 'current' into sl-access-control

website/blog/2022-08-31-august-product-update.md

@@ -22,7 +22,7 @@ You’ll hear more in [Tristan’s keynote](https://coalesce.getdbt.com/agenda/k
 
 ## **What's new**
 
-- **dbt Core v1.3 beta:** Do you use Python for analytics? The first beta prerelease of dbt Core v1.3—including support for dbt models written in Python—is [ready to explore](https://docs.getdbt.com/docs/dbt-versions/core-upgrade/upgrading-to-v1.3)! Check it out, and read more about dbt supported Python models [in our docs](/docs/build/python-models).
+- **dbt Core v1.3 beta:** Do you use Python for analytics? The first beta prerelease of dbt Core v1.3—including support for dbt models written in Python—is [ready to explore](https://docs.getdbt.com/docs/dbt-versions/core-upgrade/older%20versions/upgrading-to-v1.3)! Check it out, and read more about dbt supported Python models [in our docs](/docs/build/python-models).
 - **Technology Partner Program:** We just launched our new [Technology Partner Program](https://www.getdbt.com/blog/dbt-labs-technology-partner-program/) with 40+ friends in the Modern Data Stack to provide consistent support for seamless integrations joint-users can trust. Check our new [dbt Cloud integrations page](http://www.getdbt.com/product/integrations) for what’s available today!
 - **Single-tenant users:** dbt Cloud v1.1.60 is now available on dbt Cloud Enterprise.
 

website/dbt-versions.js

@@ -1,4 +1,8 @@
 exports.versions = [
+  {
+    version: "1.9",
+    isPrerelease: true, 
+  },
   {
     version: "1.8",
     EOLDate: "2025-04-15",

website/docs/best-practices/how-we-build-our-metrics/semantic-layer-9-conclusion.md

@@ -28,6 +28,7 @@ pagination_next: null
 
 - 🗺️ Use these best practices to map out your team's plan to **incrementally adopt the Semantic Layer**.
 - 🤗 Get involved in the community and ask questions, **help craft best practices**, and share your progress in building a dbt Semantic Layer.
+- [Validate semantic nodes in CI](/docs/deploy/ci-jobs#semantic-validations-in-ci) to ensure code changes made to dbt models don't break these metrics.
 
 The dbt Semantic Layer is the biggest paradigm shift thus far in the young practice of analytics engineering. It's ready to provide value right away, but is most impactful if you move your project towards increasing normalization, and allow MetricFlow to do the denormalization for you with maximum dimensionality.
 

website/docs/docs/build/join-logic.md

@@ -43,9 +43,9 @@ MetricFlow primarily uses left joins for joins, and restricts the use of fan-out
 
 ### Example
 
-The following example uses two semantic models with a common entity and shows a MetricFlow query that requires a join between the two semantic models. 
-
-Let's say you have two semantic models, `transactions` and `user_signup` as seen in the following example: 
+The following example uses two semantic models with a common entity and shows a MetricFlow query that requires a join between the two semantic models. The two semantic models are:
+- `transactions`
+- `user_signup`
 
 ```yaml
 semantic_models:
@@ -70,16 +70,17 @@ semantic_models:
         type: categorical
 ```
 
-MetricFlow will use `user_id` as the join key to join two semantic models, `transactions` and `user_signup`. This enables you to query the `average_purchase_price` metric in `transactions`, sliced by the `type` dimension in the `user_signup` semantic model.
-
-Note that the `average_purchase_price` measure is defined in the `transactions` semantic model, where `user_id` is a foreign entity. However, the `user_signup` semantic model has `user_id` as a primary entity. 
-
-Since this is a foreign-to-primary relationship, a left join is implemented where the `transactions` semantic model joins the `user_signup` semantic model since the `average_purchase_price` measure is defined in the `transactions` semantic model.
+- MetricFlow uses `user_id` as the join key to link two semantic models, `transactions` and `user_signup`. This allows you to query the `average_purchase_price` metric in the `transactions` semantic model, grouped by the `type` dimension in the `user_signup` semantic model.
+  - Note that the `average_purchase_price` measure is defined in `transactions`, where `user_id` is a foreign entity. However, `user_signup` has `user_id` as a primary entity. 
+- Since `user_id` is a foreign key in `transactions` and a primary key in `user_signup`, MetricFlow performs a left join where `transactions` joins `user_signup` to access the `average_purchase_price` measure defined in `transactions`.
+- To query dimensions from different semantic models, add a double underscore (or dunder) to the dimension name after joining the entity in your editing tool. The following query, `user_id__type` is included as a dimension using the `--group-by` flag (`type` is the dimension).
 
-When querying dimensions from different semantic models using the CLI, a double underscore (or dunder) is added to the dimension name after the joining entity. In the CLI query shown below, `user_id__type` is included as a `dimension`.
+```yaml 
+dbt sl query --metrics average_purchase_price --group-by metric_time,user_id__type # In dbt Cloud
+```
 
 ```yaml 
-mf query --metrics average_purchase_price --dimensions metric_time,user_id__type 
+mf query --metrics average_purchase_price --group-by metric_time,user_id__type # In dbt Core
 ```
 
 ## Multi-hop joins

website/docs/docs/build/measures.md

@@ -6,7 +6,7 @@ sidebar_label: "Measures"
 tags: [Metrics, Semantic Layer]
 ---
 
-Measures are aggregations performed on columns in your model. They can be used as final metrics or serve as building blocks for more complex metrics. 
+Measures are aggregations performed on columns in your model. They can be used as final metrics or as building blocks for more complex metrics. 
 
 Measures have several inputs, which are described in the following table along with their field types.
 
@@ -31,7 +31,7 @@ measures:
 
 ### Name
 
-When you create a measure, you can either give it a custom name or use the `name` of the data platform column directly. If the `name` of the measure is different from the column name, you need to add an `expr` to specify the column name. The `name` of the measure is used when creating a metric. 
+When you create a measure, you can either give it a custom name or use the `name` of the data platform column directly. If the measure's `name` differs from the column name, you need to add an `expr` to specify the column name. The `name` of the measure is used when creating a metric. 
 
 Measure names must be unique across all semantic models in a project and can not be the same as an existing `entity` or `dimension` within that same model.
 
@@ -88,7 +88,7 @@ If the `name` you specified for a measure doesn't match a column name in your mo
 **Notes**: When using SQL functions in the `expr` parameter, **always use data platform-specific SQL**. This is because outputs may differ depending on your specific data platform.
 
 :::tip For Snowflake users
-For Snowflake users, if you use a week-level function in the `expr` parameter, it'll now return Monday as the default week start day based on ISO standards. If you have any account or session level overrides for the `WEEK_START` parameter that fix it to a value other than 0 or 1, you will still see Monday as the week start. 
+For Snowflake users, if you use a week-level function in the `expr` parameter, it'll now return Monday as the default week start day based on ISO standards. If you have any account or session level overrides for the `WEEK_START` parameter that fixes it to a value other than 0 or 1, you will still see Monday as the week starts. 
 
 If you use the `dayofweek` function in the `expr` parameter with the legacy Snowflake default of `WEEK_START = 0`, it will now return ISO-standard values of 1 (Monday) through 7 (Sunday) instead of Snowflake's legacy default values of 0 (Monday) through 6 (Sunday).
 :::
@@ -200,7 +200,7 @@ Parameters under the `non_additive_dimension` will specify dimensions that the m
 
 ```yaml
 semantic_models:
-  - name: subscription_table
+  - name: subscription_id
     description: A subscription table with one row per date for each active user and their subscription plans. 
     model: ref('your_schema.subscription_table')
     defaults:
@@ -209,6 +209,7 @@ semantic_models:
     entities:
       - name: user_id
         type: foreign
+        primary_entity: subscription_table
 
     dimensions:
       - name: metric_time
@@ -218,22 +219,22 @@ semantic_models:
           time_granularity: day
 
     measures: 
-      - name: count_users_end_of_month 
+      - name: count_users
         description: Count of users at the end of the month 
-        expr: 1
-        agg: sum 
+        expr: user_id
+        agg: count_distinct
         non_additive_dimension: 
           name: metric_time
-          window_choice: min 
-      - name: mrr_end_of_month
-        description: Aggregate by summing all users' active subscription plans at the end of month 
+          window_choice: max 
+      - name: mrr
+        description: Aggregate by summing all users' active subscription plans
         expr: subscription_value
         agg: sum 
         non_additive_dimension: 
           name: metric_time
           window_choice: max
-      - name: mrr_by_user_end_of_month
-        description: Group by user_id to achieve each user's MRR at the end of the month 
+      - name: mrr
+        description: Group by user_id to achieve each user's MRR
         expr: subscription_value
         agg: sum  
         non_additive_dimension: 
@@ -243,26 +244,26 @@ semantic_models:
             - user_id 
 
 metrics:
-  - name: mrr_end_of_month
+  - name: mrr_metrics
     type: simple
     type_params:
-        measure: mrr_end_of_month
+        measure: mrr
 ```
 
 We can query the semi-additive metrics using the following syntax:
 
 For dbt Cloud:
 
 ```bash
-dbt sl query --metrics mrr_by_end_of_month --dimensions metric_time__month --order metric_time__month 
-dbt sl query --metrics mrr_by_end_of_month --dimensions metric_time__week --order metric_time__week 
+dbt sl query --metrics mrr_by_end_of_month --group-by metric_time__month --order metric_time__month 
+dbt sl query --metrics mrr_by_end_of_month --group-by metric_time__week --order metric_time__week 
 ```
 
 For dbt Core:
 
 ```bash
-mf query --metrics mrr_by_end_of_month --dimensions metric_time__month --order metric_time__month 
-mf query --metrics mrr_by_end_of_month --dimensions metric_time__week --order metric_time__week 
+mf query --metrics mrr_by_end_of_month --group-by metric_time__month --order metric_time__month 
+mf query --metrics mrr_by_end_of_month --group-by metric_time__week --order metric_time__week 
 ```
 
 import SetUpPages from '/snippets/_metrics-dependencies.md';

website/docs/docs/build/saved-queries.md

@@ -230,5 +230,5 @@ To include all saved queries in the dbt build run, use the [`--resource-type` fl
 </detailsToggle>
 
 ## Related docs
-
+- [Validate semantic nodes in a CI job](/docs/deploy/ci-jobs#semantic-validations-in-ci)
 - Configure [caching](/docs/use-dbt-semantic-layer/sl-cache)

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

@@ -110,17 +110,34 @@ As a tip, most command-line tools have a `--help` flag to show available command
 - `dbt run --help`: Lists the flags available for the `run` command
 :::
 
-### SQLFluff integration
-The dbt Cloud CLI supports [SQLFluff](https://sqlfluff.com/), a modular and configuration SQL linter, which warns you of complex functions, syntax, formatting, and compilation errors.
+### Lint SQL files 
 
-To get started, run `dbt sqlfluff -h` to see the list of supported commands and flags, such as `dbt sqlfluff lint` to lint SQL files.
+From the dbt Cloud CLI, you can invoke [SQLFluff](https://sqlfluff.com/) which is a modular and configurable SQL linter that warns you of complex functions, syntax, formatting, and compilation errors. Many of the same flags that you can pass to SQLFluff are available from the dbt Cloud CLI.
+
+The available SQLFluff commands are: 
+
+- `lint` — Lint SQL files by passing a list of files or from standard input (stdin).
+- `fix` — Fix SQL files.
+- `format` — Autoformat SQL files.
+
+
+To lint SQL files, run the command as follows:  
+
+```shell
+dbt sqlfluff lint [PATHS]... [flags]
+```
+
+When no path is set, dbt lints all SQL files in the current project. To lint a specific SQL file or a directory, set `PATHS` to the path of the SQL file(s) or directory of files. To lint multiple files or directories, pass multiple `PATHS` flags.  
+
+To show detailed information on all the dbt supported commands and flags, run the `dbt sqlfluff -h` command. 
 
 #### Considerations
-Keep the following points in mind when using SQLFluff with the dbt Cloud:
 
-- When you run `dbt sqlfluff`, it picks up changes to your local .sqlfluff config.
-- To use SQLFluff in continuous integration/continuous development, you need to have a `dbt_cloud.yml` file in your project and run commands from a valid dbt project.
-- SQLFluff commands in the dbt Cloud CLI do not return exit codes yet.
+When running `dbt sqlfluff` from the dbt Cloud CLI, the following are important behaviors to consider:
+
+- dbt reads the `.sqlfluff` file, if it exists, for any custom configurations you might have.
+- For continuous integration/continuous development (CI/CD) workflows, your project must have a `dbt_cloud.yml` file and you have successfully run commands from within this dbt project.
+- An SQLFluff command will return an exit code of 0 if it ran without any file violations. This dbt behavior differs from SQLFluff behavior, where a linting violation returns a non-zero exit code. dbt Labs plans on addressing this in a later release.
 
 ## FAQs
 <Expandable alt_header="How to create a .dbt directory and move your file">

website/docs/docs/cloud/manage-access/enterprise-permissions.md

@@ -29,7 +29,7 @@ The user's [license](/docs/cloud/manage-access/seats-and-users) type always over
 
 ## How to set up RBAC Groups in dbt Cloud
 
-Role-Based Access Control (RBAC) is helpful for automatically assigning permissions to dbt admins based on their SSO provider group associations.
+Role-Based Access Control (RBAC) is helpful for automatically assigning permissions to dbt admins based on their SSO provider group associations. RBAC does not apply to [model groups](/docs/collaborate/govern/model-access#groups).
 
 1. Click the gear icon to the top right and select **Account Settings**. Click **Groups & Licenses**
 

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

@@ -0,0 +1,27 @@
+---
+title: "Upgrading to v1.9 (beta)"
+id: upgrading-to-v1.9
+description: New features and changes in dbt Core v1.9
+displayed_sidebar: "docs"
+---
+
+## Resources
+
+- Changelog (coming soon)
+- [dbt Core CLI Installation guide](/docs/core/installation-overview)
+- [Cloud upgrade guide](/docs/dbt-versions/upgrade-dbt-version-in-cloud) — dbt Cloud is now versionless. dbt v1.9 will not appear in the version dropdown. Select "Keep on latest version" to get all the latest features and functionality in your dbt Cloud account.
+
+## What to know before upgrading
+
+dbt Labs is committed to providing backward compatibility for all versions 1.x, except for any changes explicitly mentioned on this page. If you encounter an error upon upgrading, please let us know by [opening an issue](https://github.com/dbt-labs/dbt-core/issues/new).
+
+
+## New and changed features and functionality
+
+Features and functionality new in dbt v1.9.
+
+**Coming soon**
+
+## Quick hits
+
+**Coming soon**

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

@@ -21,6 +21,10 @@ Release notes are grouped by month for both multi-tenant and virtual private clo
 ## July 2024
 - **Enhancement**: The dbt Semantic Layer now offers more granular control by supporting multiple data platform credentials, which can represent different roles or service accounts. Available for dbt Cloud Enteprise plans, you can map credentials to service tokens for secure authentication. Refer to [Set up dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl#set-up-dbt-semantic-layer) for more details.
 
+## July 2024
+- **New**: The ability to lint your SQL files from the dbt Cloud CLI is now available. To learn more, refer to [Lint SQL files](/docs/cloud/configure-cloud-cli#lint-sql-files).
+- **New**: Introduced Semantic validations in CI pipelines. Automatically test your semantic nodes (metrics, semantic models, and saved queries) during code reviews by adding warehouse validation checks in your CI job using the `dbt sl validate` command. You can also validate modified semantic nodes to guarantee code changes made to dbt models don't break these metrics. Refer to [Semantic validations in CI](/docs/deploy/ci-jobs#semantic-validations-in-ci) to learn about the additional commands and use cases.
+
 ## June 2024
 - **New:** Introduced new granularity support for cumulative metrics in MetricFlow. Granularity options for cumulative metrics are slightly different than granularity for other metric types. For other metrics, we use the `date_trunc` function to implement granularity. However, because cumulative metrics are non-additive (values can't be added up), we can't use the `date_trunc` function to change their time grain granularity. 
 

website/docs/docs/dbt-versions/upgrade-dbt-version-in-cloud.md

@@ -120,7 +120,7 @@ clean-targets:
 - Do you have custom scripts that parse dbt <Term id="json" /> artifacts?
 - (BigQuery only) Do you use dbt's legacy capabilities around ingestion-time-partitioned tables?
 
-If you believe your project might be affected, read more details in the migration guide [here](/docs/dbt-versions/core-upgrade/upgrading-to-v1.0).
+If you believe your project might be affected, read more details in the migration guide [here](/docs/dbt-versions/core-upgrade/older%20versions/upgrading-to-v1.0).
 
 </details>