Merge branch 'current' into ly-docs-slack-prereq

website/docs/docs/build/custom-schemas.md

@@ -4,26 +4,29 @@ id: "custom-schemas"
 pagination_next: "docs/build/custom-databases"
 ---
 
-By default, all dbt models are built in the schema specified in your target. In dbt projects with lots of models, it may be useful to instead build some models in schemas other than your target schema – this can help logically group models together.
+By default, all dbt models are built in the schema specified in your [environment](/docs/dbt-cloud-environments) (dbt Cloud) or [profile's target](/docs/core/dbt-core-environments) (dbt Core). This default schema is called your _target schema_.
 
-For example, you may wish to:
-* Group models based on the business unit using the model, creating schemas such as `core`, `marketing`, `finance` and `support`; or,
+For dbt projects with lots of models, it's common to build models across multiple schemas and group similar models together. For example, you might want to:
+
+* Group models based on the business unit using the model, creating schemas such as `core`, `marketing`, `finance` and `support`.
 * Hide intermediate models in a `staging` schema, and only present models that should be queried by an end user in an `analytics` schema.
 
-You can use **custom schemas** in dbt to build models in a schema other than your target schema. It's important to note that by default, dbt will generate the schema name for a model by **concatenating the custom schema to the target schema**, as in: `<target_schema>_<custom_schema>;`.
+To do this, specify a custom schema. dbt generates the schema name for a model by appending the custom schema to the target schema. For example, `<target_schema>_<custom_schema>`.
 
 | Target schema | Custom schema | Resulting schema |
 | ------------- | ------------- | ---------------- |
-| &lt;target_schema&gt; | None | &lt;target_schema&gt; |
-| analytics | None | analytics |
-| dbt_alice | None | dbt_alice |
-| &lt;target_schema&gt; | &lt;custom_schema&gt; | &lt;target_schema&gt;\_&lt;custom_schema&gt; |
-| analytics | marketing | analytics_marketing |
-| dbt_alice | marketing | dbt_alice_marketing |
+| analytics_prod | None | analytics_prod |
+| alice_dev | None | alice_dev |
+| dbt_cloud_pr_123_456 | None | dbt_cloud_pr_123_456 |
+| analytics_prod | marketing | analytics_prod_marketing |
+| alice_dev | marketing | alice_dev_marketing |
+| dbt_cloud_pr_123_456 | marketing | dbt_cloud_pr_123_456_marketing |
 
 ## How do I use custom schemas?
-Use the `schema` configuration key to specify a custom schema for a model. As with any configuration, you can either:
-* apply this configuration to a specific model by using a config block within a model, or
+
+To specify a custom schema for a model, use the `schema` configuration key. As with any configuration, you can do one of the following:
+
+* apply this configuration to a specific model by using a config block within a model
 * apply it to a subdirectory of models by specifying it in your `dbt_project.yml` file
 
 <File name='orders.sql'>
@@ -36,12 +39,10 @@ select ...
 
 </File>
 
-
-
 <File name='dbt_project.yml'>
 
 ```yaml
-# models in `models/marketing/ will be rendered to the "*_marketing" schema
+# models in `models/marketing/ will be built in the "*_marketing" schema
 models:
   my_project:
     marketing:
@@ -52,17 +53,17 @@ models:
 
 ## Understanding custom schemas
 
-When first using custom schemas, it's common to assume that a model will be built in a schema that matches the `schema` configuration exactly, for example, a model that has the configuration `schema: marketing`, would be built in the `marketing` schema. However, dbt instead creates it in a schema like `<target_schema>_marketing` by default – there's a good reason for this!
+When first using custom schemas, it's a common misunderstanding to assume that a model _only_ uses the new `schema` configuration; for example, a model that has the configuration `schema: marketing` would be built in the `marketing` schema. However, dbt puts it in a schema like `<target_schema>_marketing`.
 
-In a typical setup of dbt, each dbt user will use a separate target schema (see [Managing Environments](/docs/build/custom-schemas#managing-environments)). If dbt created models in a schema that matches a model's custom schema exactly, every dbt user would create models in the same schema.
+There's a good reason for this deviation. Each dbt user has their own target schema for development (refer to [Managing Environments](#managing-environments)). If dbt ignored the target schema and only used the model's custom schema, every dbt user would create models in the same schema and would overwrite each other's work.
 
-Further, the schema that your development models are built in would be the same schema that your production models are built in! Instead, concatenating the custom schema to the target schema helps create distinct schema names, reducing naming conflicts.
+By combining the target schema and the custom schema, dbt ensures that objects it creates in your data warehouse don't collide with one another.
 
 If you prefer to use different logic for generating a schema name, you can change the way dbt generates a schema name (see below).
 
 ### How does dbt generate a model's schema name?
 
-dbt uses a default macro called `generate_schema_name` to determine the name of the schema that a model should be built in. 
+dbt uses a default macro called `generate_schema_name` to determine the name of the schema that a model should be built in.
 
 The following code represents the default macro's logic:
 
@@ -83,30 +84,23 @@ The following code represents the default macro's logic:
 {%- endmacro %}
 ```
 
-## Advanced custom schema configuration
-
-You can customize schema name generation in dbt depending on your needs, such as creating a custom macro named `generate_schema_name` in your project or using the built-in macro for environment-based schema names. The built-in macro follows a pattern of generating schema names based on the environment, making it a convenient alternative.
-
-If your dbt project has a macro that’s also named `generate_schema_name`, dbt will always use the macro in your dbt project instead of the default macro.
-
-### Changing the way dbt generates a schema name
+## Changing the way dbt generates a schema name
 
-To modify how dbt generates schema names, you should add a macro named `generate_schema_name` to your project and customize it according to your needs:
+If your dbt project has a custom macro called `generate_schema_name`, dbt will use it instead of the default macro. This allows you to customize the name generation according to your needs.
 
-- Copy and paste the `generate_schema_name` macro into a file named 'generate_schema_name'.
+To customize this macro, copy the example code in the section [How does dbt generate a model's schema name](#how-does-dbt-generate-a-models-schema-name) into a file named `macros/generate_schema_name.sql` and make changes as necessary.
 
-- Modify the target schema by either using [target variables](/reference/dbt-jinja-functions/target) or [env_var](/reference/dbt-jinja-functions/env_var). Check out our [Advanced Deployment - Custom Environment and job behavior](https://courses.getdbt.com/courses/advanced-deployment) course video for more details. 
-
-**Note**: dbt will ignore any custom `generate_schema_name` macros included in installed packages. 
+Be careful. dbt will ignore any custom `generate_schema_name` macros included in installed packages.
 
 <details>
 <summary>❗️ Warning: Don't replace <code>default_schema</code> in the macro.</summary>
 
-If you're modifying how dbt generates schema names, don't just replace ```{{ default_schema }}_{{ custom_schema_name | trim }}``` with ```{{ custom_schema_name | trim }}``` in the ```generate_schema_name``` macro. 
+If you're modifying how dbt generates schema names, don't just replace ```{{ default_schema }}_{{ custom_schema_name | trim }}``` with ```{{ custom_schema_name | trim }}``` in the ```generate_schema_name``` macro.
 
 If you remove ```{{ default_schema }}```, it causes developers to override each other's models if they create their own custom schemas. This can also cause issues during development and continuous integration (CI).
 
-❌ The following code block is an example of what your code _should not_ look like: 
+❌ The following code block is an example of what your code _should not_ look like:
+
 ```sql
 {% macro generate_schema_name(custom_schema_name, node) -%}
 
@@ -123,39 +117,9 @@ If you remove ```{{ default_schema }}```, it causes developers to override each
 
 {%- endmacro %}
 
-```
-</details>
-
-### An alternative pattern for generating schema names
-
-A common way to generate schema names is by adjusting the behavior according to the environment in dbt. Here's how it works:
-
-**Production environment**
-
-- If a custom schema is specified, the schema name of a model should match the custom schema, instead of concatenating to the target schema.
-- If no custom schema is specified, the schema name of a model should match the target schema.
-
-**Other environments** (like development or quality assurance (QA)):
-
-- Build _all_ models in the target schema, ignoring any custom schema configurations.
-
-dbt ships with a global, predefined macro that contains this logic - `generate_schema_name_for_env`. 
-
-If you want to use this pattern, you'll need a `generate_schema_name` macro in your project that points to this logic. You can do this by creating a file in your `macros` directory (typically named `get_custom_schema.sql`), and copying/pasting the following code:
-
-<File name='macros/get_custom_schema.sql'>
-
-```sql
--- put this in macros/get_custom_schema.sql
-
-{% macro generate_schema_name(custom_schema_name, node) -%}
-    {{ generate_schema_name_for_env(custom_schema_name, node) }}
-{%- endmacro %}
 ```
 
-</File>
-
-**Note:** When using this macro, you'll need to set the target name in your job specifically to "prod" if you want custom schemas to be applied.
+</details>
 
 ### generate_schema_name arguments
 
@@ -165,6 +129,7 @@ If you want to use this pattern, you'll need a `generate_schema_name` macro in y
 | node | The `node` that is currently being processed by dbt | `{"name": "my_model", "resource_type": "model",...}` |
 
 ### Jinja context available in generate_schema_name
+
 If you choose to write custom logic to generate a schema name, it's worth noting that not all variables and methods are available to you when defining this logic. In other words: the `generate_schema_name` macro is compiled with a limited Jinja context.
 
 The following context methods _are_ available in the `generate_schema_name` macro:
@@ -192,13 +157,52 @@ See docs on macro `dispatch`: ["Managing different global overrides across packa
 
 </VersionBlock>
 
+## A built-in alternative pattern for generating schema names
+
+A common customization is to ignore the target schema in production environments, and ignore the custom schema configurations in other environments (such as development and CI).
+
+Production Environment (`target.name == 'prod'`)
+
+| Target schema | Custom schema | Resulting schema |
+| ------------- | ------------- | ---------------- |
+| analytics_prod | None | analytics_prod |
+| analytics_prod | marketing | marketing |
+
+Development/CI Environment (`target.name != 'prod'`)
+
+| Target schema | Custom schema | Resulting schema |
+| ------------- | ------------- | ---------------- |
+| alice_dev | None | alice_dev |
+| alice_dev | marketing | alice_dev |
+| dbt_cloud_pr_123_456 | None | dbt_cloud_pr_123_456 |
+| dbt_cloud_pr_123_456 | marketing | dbt_cloud_pr_123_456 |
+
+Similar to the regular macro, this approach guarantees that schemas from different environments will not collide.
+
+dbt ships with a macro for this use case &mdash; called `generate_schema_name_for_env` &mdash; which is disabled by default. To enable it, add a custom `generate_schema_name` macro to your project that contains the following code:
+
+<File name='macros/get_custom_schema.sql'>
+
+```sql
+-- put this in macros/get_custom_schema.sql
+
+{% macro generate_schema_name(custom_schema_name, node) -%}
+    {{ generate_schema_name_for_env(custom_schema_name, node) }}
+{%- endmacro %}
+```
+
+</File>
+
+When using this macro, you'll need to set the target name in your production job to `prod`.
+
 ## Managing environments
 
-In the `generate_schema_name` macro examples shown above, the `target.name` context variable is used to change the schema name that dbt generates for models. If the `generate_schema_name` macro in your project uses the `target.name` context variable, you must additionally ensure that your different dbt environments are configured appropriately. While you can use any naming scheme you'd like, we typically recommend:
- - **dev**: Your local development environment; configured in a `profiles.yml` file on your computer.
-* **ci**:  A [continuous integration](/docs/cloud/git/connect-github) environment running on Pull Requests in GitHub, GitLab, etc.
- - **prod**: The production deployment of your dbt project, like in dbt Cloud, Airflow, or [similar](/docs/deploy/deployments).
+In the `generate_schema_name` macro examples shown in the [built-in alternative pattern](#a-built-in-alternative-pattern-for-generating-schema-names) section, the `target.name` context variable is used to change the schema name that dbt generates for models. If the `generate_schema_name` macro in your project uses the `target.name` context variable, you must ensure that your different dbt environments are configured accordingly. While you can use any naming scheme you'd like, we typically recommend:
+
+* **dev** &mdash; Your local development environment; configured in a `profiles.yml` file on your computer.
+* **ci** &mdash; A [continuous integration](/docs/cloud/git/connect-github) environment running on pull pequests in GitHub, GitLab, and so on.
+* **prod** &mdash; The production deployment of your dbt project, like in dbt Cloud, Airflow, or [similar](/docs/deploy/deployments).
 
-If your schema names are being generated incorrectly, double check your target name in the relevant environment.
+If your schema names are being generated incorrectly, double-check your target name in the relevant environment.
 
 For more information, consult the [managing environments in dbt Core](/docs/core/dbt-core-environments) guide.

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

@@ -33,6 +33,4 @@ saved_queries:
         - "{{ Dimension('listing__capacity_latest') }} > 3"
 ```
 
-### FAQs 
-
-* All metrics in a saved query need to use the same dimensions in the `group_by` or `where` clauses.
+All metrics in a saved query need to use the same dimensions in the `group_by` or `where` clauses.