Merge branch 'current' into expand-releases

.github/workflows/lint.yml

@@ -12,8 +12,16 @@ jobs:
       uses: actions/setup-node@v3
       with:
         node-version: '18.12.0'
+
+    - name: Cache Node Modules
+      uses: actions/cache@v3
+      id: cache-node-mods
+      with:
+        path: website/node_modules
+        key: node-modules-cache-v3-${{ hashFiles('**/package.json', '**/package-lock.json') }}
 
     - name: Install Packages
+      if: steps.cache-node-mods.outputs.cache-hit != 'true'
       run: cd website && npm ci
 
     - name: Run ESLint

website/docs/docs/about-setup.md

@@ -21,14 +21,14 @@ To begin configuring dbt now, select the option that is right for you.
 
 <Card
     title="dbt Cloud setup"
-    body="Learn how to connect to a data platform, integrate with secure authentication methods, configure a sync with a git repo, how to use the IDE, and how to install the dbt Cloud CLI."
+    body="Learn how to connect to a data platform, integrate with secure authentication methods, and configure a sync with a git repo."
     link="/docs/cloud/about-cloud-setup"
     icon="dbt-bit"/>
 
 <Card
-    title="dbt Core installation"
-    body="Learn how to connect install dbt Core using Pip, Homebrew, Docker, or the open source repo."
-    link="/docs/core/installation"
+    title="dbt Core setup"
+    body="Learn about dbt Core and how to setup data platform connections."
+    link="/docs/core/about-core-setup"
     icon="dbt-bit"/>
 
 </div>

website/docs/docs/build/about-metricflow.md

@@ -82,7 +82,7 @@ The following example data is based on the Jaffle Shop repo. You can view the co
 To make this more concrete, consider the metric `order_total`, which is defined using the SQL expression:
 
 `select sum(order_total) as order_total from orders` 
-This expression calculates the revenue from each order by summing the order_total column in the orders table. In a business setting, the metric order_total is often calculated according to different categories, such as"
+This expression calculates the total revenue for all orders by summing the order_total column in the orders table. In a business setting, the metric order_total is often calculated according to different categories, such as"
 - Time, for example `date_trunc(ordered_at, 'day')`
 - Order Type, using `is_food_order` dimension from the `orders` table.
 

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

@@ -14,7 +14,7 @@ Use MetricFlow in dbt to centrally define your metrics. As a key component of th
 
 MetricFlow allows you to:
 - Intuitively define metrics in your dbt project
-- Develop from your preferred environment, whether that's the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation), [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud), or [dbt Core](/docs/core/installation)
+- Develop from your preferred environment, whether that's the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation), [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud), or [dbt Core](/docs/core/installation-overview)
 - Use [MetricFlow commands](/docs/build/metricflow-commands) to query and test those metrics in your development environment 
 - Harness the true magic of the universal dbt Semantic Layer and dynamically query these metrics in downstream tools (Available for dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) accounts only).
 

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

@@ -38,10 +38,7 @@ metrics:
 
 ## Limitations
 Cumulative metrics are currently under active development and have the following limitations:
-
-1. You can only use the [`metric_time` dimension](/docs/build/dimensions#time) to check cumulative metrics. If you don't use `metric_time` in the query, the cumulative metric will return incorrect results because it won't perform the time spine join. This means you cannot reference time dimensions other than the `metric_time` in the query.
-2. If you use `metric_time` in your query filter but don't include "start_time" and "end_time," cumulative metrics will left-censor the input data. For example, if you query a cumulative metric with a 7-day window with the filter `{{ TimeDimension('metric_time') }} BETWEEN '2023-08-15' AND '2023-08-30' `, the values for `2023-08-15` to `2023-08-20` return missing or incomplete data. This is because we apply the `metric_time` filter to the aggregation input. To avoid this, you must use `start_time` and `end_time` in the query filter.
-
+- You are required to use [`metric_time` dimension](/docs/build/dimensions#time) when querying cumulative metrics. If you don't use `metric_time` in the query, the cumulative metric will return incorrect results because it won't perform the time spine join. This means you cannot reference time dimensions other than the `metric_time` in the query.
 
 ## Cumulative metrics example
 

website/docs/docs/build/dimensions.md

@@ -15,7 +15,8 @@ In a data platform, dimensions is part of a larger structure called a semantic m
 
 Groups are defined within semantic models, alongside entities and measures, and correspond to non-aggregatable columns in your dbt model that provides categorical or time-based context. In SQL, dimensions  is typically included in the GROUP BY clause.-->
 
-All dimensions require a `name`, `type` and in some cases, an `expr` parameter. 
+All dimensions require a `name`, `type` and in some cases, an `expr` parameter. The `name` for your dimension must be unique to the semantic model and can not be the same as an existing `entity` or `measure` within that same model.
+
 
 | Parameter | Description | Type |
 | --------- | ----------- | ---- |

website/docs/docs/build/entities.md

@@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer]
 
 Entities are real-world concepts in a business such as customers, transactions, and ad campaigns. We often focus our analyses around specific entities, such as customer churn or annual recurring revenue modeling. We represent entities in our semantic models using id columns that serve as join keys to other semantic models in your semantic graph.
 
-Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter.
+Within a semantic graph, the required parameters for an entity are `name` and `type`. The `name` refers to either the key column name from the underlying data table, or it may serve as an alias with the column name referenced in the `expr` parameter. The `name` for your entity must be unique to the semantic model and can not be the same as an existing `measure` or `dimension` within that same model.
 
 Entities can be specified with a single column or multiple columns. Entities (join keys) in a semantic model are identified by their name. Each entity name must be unique within a semantic model, but it doesn't have to be unique across different semantic models. 
 

website/docs/docs/build/measures.md

@@ -34,7 +34,8 @@ measures:
 
 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. 
 
-Measure names must be **unique** across all semantic models in a project.
+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.
+
 
 ### Description
 

website/docs/docs/build/metricflow-commands.md

@@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer]
 
 Once you define metrics in your dbt project, you can query metrics, dimensions, and dimension values, and validate your configs using the MetricFlow commands. 
 
-MetricFlow allows you to define and query metrics in your dbt project in the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation), [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud), or [dbt Core](/docs/core/installation). To experience the power of the universal [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and dynamically query those metrics in downstream tools, you'll need a dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) account. 
+MetricFlow allows you to define and query metrics in your dbt project in the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation), [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud), or [dbt Core](/docs/core/installation-overview). To experience the power of the universal [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and dynamically query those metrics in downstream tools, you'll need a dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) account. 
 
 MetricFlow is compatible with Python versions 3.8, 3.9, 3.10, and 3.11.
 

website/docs/docs/build/packages.md

@@ -25,15 +25,9 @@ dbt _packages_ are in fact standalone dbt projects, with models and macros that
 * It's important to note that defining and installing dbt packages is different from [defining and installing Python packages](/docs/build/python-models#using-pypi-packages)
 
 
-:::info `dependencies.yml` has replaced `packages.yml`
-Starting from dbt v1.6, `dependencies.yml` has replaced `packages.yml`. This file can now contain both types of dependencies: "package" and "project" dependencies.
-- "Package" dependencies lets you add source code from someone else's dbt project into your own, like a library. 
-- "Project" dependencies provide a different way to build on top of someone else's work in dbt. Refer to [Project dependencies](/docs/collaborate/govern/project-dependencies) for more info.
-- 
-You can rename `packages.yml` to `dependencies.yml`, _unless_ you need to use Jinja within your packages specification. This could be necessary, for example, if you want to add an environment variable with a git token in a private git package specification.
-
-:::
+import UseCaseInfo from '/snippets/_packages_or_dependencies.md';
 
+<UseCaseInfo/>
 
 ## How do I add a package to my project?
 1. Add a file named <VersionBlock firstVersion="1.6"> `dependencies.yml` or </VersionBlock> `packages.yml` to your dbt project. This should be at the same level as your `dbt_project.yml` file.

website/docs/docs/cloud/about-cloud-setup.md

@@ -13,14 +13,13 @@ dbt Cloud is the fastest and most reliable way to deploy your dbt jobs. It conta
 - Configuring access to [GitHub](/docs/cloud/git/connect-github), [GitLab](/docs/cloud/git/connect-gitlab), or your own [git repo URL](/docs/cloud/git/import-a-project-by-git-url).
 - [Managing users and licenses](/docs/cloud/manage-access/seats-and-users)
 - [Configuring secure access](/docs/cloud/manage-access/about-user-access)
-- Configuring the [dbt Cloud IDE](/docs/cloud/about-cloud-develop)
-- Installing and configuring the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation)
 
 These settings are intended for dbt Cloud administrators. If you need a more detailed first-time setup guide for specific data platforms, read our [quickstart guides](/guides).
 
 If you want a more in-depth learning experience, we recommend taking the dbt Fundamentals on our [dbt Learn online courses site](https://courses.getdbt.com/).
 
 ## Prerequisites
+
 - To set up dbt Cloud, you'll need to have a dbt Cloud account with administrator access. If you still need to create a dbt Cloud account, [sign up today](https://getdbt.com) on our North American servers or [contact us](https://getdbt.com/contact) for international options.
 - To have the best experience using dbt Cloud, we recommend you use modern and up-to-date web browsers like Chrome, Safari, Edge, and Firefox.
 

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

@@ -0,0 +1,30 @@
+---
+title: About developing in dbt
+id: about-develop-dbt
+description: "Learn how to develop your dbt projects using dbt Cloud."
+sidebar_label: "About developing in dbt" 
+pagination_next: "docs/cloud/about-cloud-develop-defer"
+hide_table_of_contents: true
+---
+
+Develop dbt projects using dbt Cloud, which offers a fast and reliable way to work on your dbt project. It runs dbt Core in a hosted (single or multi-tenant) environment.
+
+You can develop in your browser using an integrated development environment (IDE) or in a dbt Cloud-powered command line interface (CLI).
+
+<div className="grid--2-col" >
+
+<Card
+    title="dbt Cloud CLI"
+    body="Allows you to develop and run dbt commands from your local command line or code editor against your dbt Cloud development environment."
+    link="/docs/cloud/cloud-cli-installation"
+    icon="dbt-bit"/>
+
+  <Card
+    title="dbt Cloud IDE"
+    body="Develop directly in your browser, making dbt project development efficient by compiling code into SQL and managing project changes seamlessly using an intuitive user interface."
+    link="/docs/cloud/dbt-cloud-ide/develop-in-the-cloud"
+    icon="dbt-bit"/>
+
+</div><br />
+
+To get started with dbt development, you'll need a [dbt Cloud](https://www.getdbt.com/signup) account and developer seat. For a more comprehensive guide about developing in dbt, refer to our [quickstart guides](/guides).

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

@@ -3,14 +3,15 @@ title: "About data platform connections"
 id: about-connections
 description: "Information about data platform connections"
 sidebar_label: "About data platform connections"
-pagination_next: "docs/cloud/connect-data-platform/connect-starburst-trino"
+pagination_next: "docs/cloud/connect-data-platform/connect-microsoft-fabric"
 pagination_prev: null
 ---
 dbt Cloud can connect with a variety of data platform providers including: 
 - [Amazon Redshift](/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb) 
 - [Apache Spark](/docs/cloud/connect-data-platform/connect-apache-spark) 
 - [Databricks](/docs/cloud/connect-data-platform/connect-databricks) 
 - [Google BigQuery](/docs/cloud/connect-data-platform/connect-bigquery)
+- [Microsoft Fabric](/docs/cloud/connect-data-platform/connect-microsoft-fabric)
 - [PostgreSQL](/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb)
 - [Snowflake](/docs/cloud/connect-data-platform/connect-snowflake)
 - [Starburst or Trino](/docs/cloud/connect-data-platform/connect-starburst-trino)

website/docs/docs/cloud/connect-data-platform/connect-microsoft-fabric.md

@@ -0,0 +1,43 @@
+---
+title: "Connect Microsoft Fabric"
+description: "Configure Microsoft Fabric connection."
+sidebar_label: "Connect Microsoft Fabric"
+---
+
+## Supported authentication methods
+The supported authentication methods are: 
+- Azure Active Directory (Azure AD) service principal
+- Azure AD password
+
+SQL password (LDAP) is not supported in Microsoft Fabric Synapse Data Warehouse so you must use Azure AD. This means that to use [Microsoft Fabric](https://www.microsoft.com/en-us/microsoft-fabric) in dbt Cloud, you will need at least one Azure AD service principal to connect dbt Cloud to Fabric, ideally one service principal for each user.
+
+### Active Directory service principal 
+The following are the required fields for setting up a connection with a Microsoft Fabric using Azure AD service principal authentication. 
+
+| Field | Description |
+| --- | --- |
+| **Server** | The service principal's **host** value for the Fabric test endpoint. |
+| **Port** | The port to connect to Microsoft Fabric. You can use `1433` (the default), which is the standard SQL server port number. |
+| **Database** | The service principal's **database** value for the Fabric test endpoint. |
+| **Authentication** | Choose **Service Principal** from the dropdown. | 
+| **Tenant ID** | The service principal's **Directory (tenant) ID**. |
+| **Client ID** | The service principal's **application (client) ID id**. |
+| **Client secret** | The service principal's **client secret** (not the **client secret id**). |  
+
+
+### Active Directory password 
+
+The following are the required fields for setting up a connection with a Microsoft Fabric using Azure AD password authentication. 
+
+| Field | Description |
+| --- | --- |
+| **Server** | The server hostname to connect to Microsoft Fabric. |
+| **Port** | The server port. You can use `1433` (the default), which is the standard SQL server port number. |
+| **Database** | The database name. |
+| **Authentication** | Choose **Active Directory Password** from the dropdown. | 
+| **User** | The AD username. |
+| **Password** | The AD username's password. |
+
+## Configuration 
+
+To learn how to optimize performance with data platform-specific configurations in dbt Cloud, refer to [Microsoft Fabric DWH configurations](/reference/resource-configs/fabric-configs).

website/docs/docs/cloud/manage-access/auth0-migration.md

@@ -4,11 +4,6 @@ id: "auth0-migration"
 sidebar: "SSO Auth0 Migration"
 description: "Required actions for migrating to Auth0 for SSO services on dbt Cloud."
 ---
-:::warning Limited availability
-
-This is a new feature that is being implemented incrementally to customers using single sign-on features today. If you have any questions or concerns about the availability of the migration feature, please [contact support](mailto:[email protected]).
-
-:::
 
 dbt Labs is partnering with Auth0 to bring enhanced features to dbt Cloud's single sign-on (SSO) capabilities. Auth0 is an identity and access management (IAM) platform with advanced security features, and it will be leveraged by dbt Cloud. These changes will require some action from customers with SSO configured in dbt Cloud today, and this guide will outline the necessary changes for each environment.