This branch was auto-updated!

website/docs/docs/build/data-tests.md

@@ -70,8 +70,6 @@ The name of this test is the name of the file: `assert_total_payment_amount_is_p
 
 Singular data tests are easy to write—so easy that you may find yourself writing the same basic structure over and over, only changing the name of a column or model. By that point, the test isn't so singular! In that case, we recommend...
 
-
-
 ## Generic data tests
 Certain data tests are generic: they can be reused over and over again. A generic data test is defined in a `test` block, which contains a parametrized query and accepts arguments. It might look like:
 
@@ -304,7 +302,6 @@ data_tests:
 
 </File>
 
-To suppress warnings about the rename, add `TestsConfigDeprecation` to the `silence` block of the `warn_error_options` flag in `dbt_project.yml`, [as described in the Warnings documentation](https://docs.getdbt.com/reference/global-configs/warnings).
 
 </VersionBlock>
 

website/docs/docs/build/documentation.md

@@ -101,7 +101,18 @@ The events in this table are recorded by [Snowplow](http://github.com/snowplow/s
 In the above example, a docs block named `table_events` is defined with some descriptive markdown contents. There is nothing significant about the name `table_events` — docs blocks can be named however you like, as long as the name only contains alphanumeric and underscore characters and does not start with a numeric character.
 
 ### Placement
-Docs blocks should be placed in files with a `.md` file extension. By default, dbt will search in all resource paths for docs blocks (i.e. the combined list of [model-paths](/reference/project-configs/model-paths), [seed-paths](/reference/project-configs/seed-paths), [analysis-paths](/reference/project-configs/analysis-paths), [macro-paths](/reference/project-configs/macro-paths) and [snapshot-paths](/reference/project-configs/snapshot-paths)) — you can adjust this behavior using the [docs-paths](/reference/project-configs/docs-paths) config.
+
+<VersionBlock firstVersion="1.9">
+
+Docs blocks should be placed in files with a `.md` file extension. By default, dbt will search in all resource paths for docs blocks (for example, the combined list of [model-paths](/reference/project-configs/model-paths), [seed-paths](/reference/project-configs/seed-paths), [analysis-paths](/reference/project-configs/analysis-paths), [test-paths](/reference/project-configs/test-paths), [macro-paths](/reference/project-configs/macro-paths), and [snapshot-paths](/reference/project-configs/snapshot-paths)) &mdash; you can adjust this behavior using the [docs-paths](/reference/project-configs/docs-paths) config.
+
+</VersionBlock>
+
+<VersionBlock lastVersion="1.8">
+
+Docs blocks should be placed in files with a `.md` file extension. By default, dbt will search in all resource paths for docs blocks (for example, the combined list of [model-paths](/reference/project-configs/model-paths), [seed-paths](/reference/project-configs/seed-paths), [analysis-paths](/reference/project-configs/analysis-paths), [macro-paths](/reference/project-configs/macro-paths), and [snapshot-paths](/reference/project-configs/snapshot-paths)) &mdash; you can adjust this behavior using the [docs-paths](/reference/project-configs/docs-paths) config.
+
+</VersionBlock>
 
 
 ### Usage

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

@@ -59,7 +59,6 @@ The following table lists the commands compatible with the dbt Cloud IDE and dbt
 
 | <div style={{width:'250px'}}>Command</div>  | <div style={{width:'100px'}}>Description</div> | dbt Cloud IDE | dbt Cloud CLI |
 |---------|-------------|---------------|---------------|
-| [`list`](#list) | Retrieves metadata values. | ✅ | ✅ |
 | [`list metrics`](#list-metrics) | Lists metrics with dimensions. |  ✅ | ✅ |
 | [`list dimensions`](#list) | Lists unique dimensions for metrics. |  ✅  | ✅ |
 | [`list dimension-values`](#list-dimension-values) | List dimensions with metrics. | ✅ | ✅ |
@@ -94,7 +93,6 @@ Check out the following video for a short video demo of how to query or preview
 
 Use the `mf` prefix before the command name to execute them in dbt Core. For example, to list all metrics, run `mf list metrics`.
 
-- [`list`](#list) &mdash; Retrieves metadata values.
 - [`list metrics`](#list-metrics) &mdash; Lists metrics with dimensions.
 - [`list dimensions`](#list) &mdash; Lists unique dimensions for metrics.
 - [`list dimension-values`](#list-dimension-values) &mdash; List dimensions with metrics.
@@ -107,17 +105,7 @@ Use the `mf` prefix before the command name to execute them in dbt Core. For exa
 </TabItem>
 </Tabs>
 
-### List
-
-This command retrieves metadata values related to [Metrics](/docs/build/metrics-overview), [Dimensions](/docs/build/dimensions), and [Entities](/docs/build/entities) values. 
-
-
 ### List metrics
-
-```bash
-dbt sl list # In dbt Cloud
-mf list # In dbt Core
-```
 This command lists the metrics with their available dimensions:
 
 ```bash
@@ -350,13 +338,13 @@ mf query --metrics order_total,users_active --group-by metric_time # In dbt Core
 
 <TabItem value="eg2" label="Dimensions">
 
-You can include multiple dimensions in a query. For example, you can group by the `is_food_order` dimension to confirm if orders were for food or not. 
+You can include multiple dimensions in a query. For example, you can group by the `is_food_order` dimension to confirm if orders were for food or not.  Note that when you query a dimension, you need to specify the primary entity for that dimension. In the following example, the primary entity is `order_id`.
 
 **Query**
 ```bash
-dbt sl query --metrics order_total --group-by metric_time,is_food_order # In dbt Cloud
+dbt sl query --metrics order_total --group-by order_id__is_food_order # In dbt Cloud
 
-mf query --metrics order_total --group-by metric_time,is_food_order # In dbt Core
+mf query --metrics order_total --group-by order_id__is_food_order # In dbt Core
 ```
 
 **Result**
@@ -380,13 +368,15 @@ mf query --metrics order_total --group-by metric_time,is_food_order # In dbt Cor
 
 You can add order and limit functions to filter and present the data in a readable format. The following query limits the data set to 10 records and orders them by `metric_time`, descending. Note that using the `-` prefix will sort the query in descending order. Without the `-` prefix sorts the query in ascending order.
 
+ Note that when you query a dimension, you need to specify the primary entity for that dimension. In the following example, the primary entity is `order_id`.
+
 **Query**
 ```bash
 # In dbt Cloud 
-dbt sl query --metrics order_total --group-by metric_time,is_food_order --limit 10 --order-by -metric_time 
+dbt sl query --metrics order_total --group-by order_id__is_food_order --limit 10 --order-by -metric_time 
 
 # In dbt Core
-mf query --metrics order_total --group-by metric_time,is_food_order --limit 10 --order-by -metric_time 
+mf query --metrics order_total --group-by order_id__is_food_order --limit 10 --order-by -metric_time 
 ```
 
 **Result**
@@ -406,15 +396,15 @@ mf query --metrics order_total --group-by metric_time,is_food_order --limit 10 -
 
 <TabItem value="eg4" label="where clause">
 
-You can further filter the data set by adding a `where` clause to your query. The following example shows you how to query the `order_total` metric, grouped by `metric_time` with multiple where statements (orders that are food orders and orders from the week starting on or after Feb 1st, 2024):
+You can further filter the data set by adding a `where` clause to your query. The following example shows you how to query the `order_total` metric, grouped by `is_food_order` with multiple where statements (orders that are food orders and orders from the week starting on or after Feb 1st, 2024). Note that when you query a dimension, you need to specify the primary entity for that dimension. In the following example, the primary entity is `order_id`.
 
 **Query**
 ```bash
 # In dbt Cloud 
-dbt sl query --metrics order_total --group-by metric_time --where "{{ Dimension('order_id__is_food_order') }} = True and metric_time__week >= '2024-02-01'"
+dbt sl query --metrics order_total --group-by order_id__is_food_order --where "{{ Dimension('order_id__is_food_order') }} = True and metric_time__week >= '2024-02-01'"
 
 # In dbt Core
-mf query --metrics order_total --group-by metric_time --where "{{ Dimension('order_id__is_food_order') }} = True and metric_time__week >= '2024-02-01'" 
+mf query --metrics order_total --group-by order_id__is_food_order --where "{{ Dimension('order_id__is_food_order') }} = True and metric_time__week >= '2024-02-01'" 
 ```
 
 **Result**
@@ -440,16 +430,16 @@ mf query --metrics order_total --group-by metric_time --where "{{ Dimension('ord
 
 To filter by time, there are dedicated start and end time options. Using these options to filter by time allows MetricFlow to further optimize query performance by pushing down the where filter when appropriate. 
 
-
+ Note that when you query a dimension, you need to specify the primary entity for that dimension. In the following example, the primary entity is `order_id`.
 <!--
 bash not support in cloud yet
 # In dbt Cloud
-dbt sl query --metrics order_total --group-by metric_time,is_food_order --limit 10 --order-by -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' 
+dbt sl query --metrics order_total --group-by order_id__is_food_order --limit 10 --order-by -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' 
 -->
 **Query**
 ```bash
 # In dbt Core
-mf query --metrics order_total --group-by metric_time,is_food_order --limit 10 --order-by -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' 
+mf query --metrics order_total --group-by order_id__is_food_order --limit 10 --order-by -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' 
 ```
 
  **Result**

website/docs/docs/build/metricflow-time-spine.md

@@ -18,10 +18,14 @@ MetricFlow requires you to define a time-spine table as a model-level configurat
 To see the generated SQL for the metric and dimension types that use time-spine joins, refer to the respective documentation or add the `compile=True` flag when querying the Semantic Layer to return the compiled SQL.
 
 ## Configuring time-spine in YAML
+
+- The time spine is a special model that tells dbt and MetricFlow how to use specific columns by defining their properties.
+- The [`models` key](/reference/model-properties) for the time spine must be in your `models/` directory.
 - You only need to configure time-spine models that the Semantic Layer should recognize.
 - At a minimum, define a time-spine table for a daily grain.
 - You can optionally define a time-spine table for a different granularity, like hourly.
-- Note that if you don’t have a date or calendar model in your project, you'll need to create one. 
+- Note that if you don’t have a date or calendar model in your project, you'll need to create one.
+
 - If you're looking to specify the grain of a time dimension so that MetricFlow can transform the underlying column to the required granularity, refer to the [Time granularity documentation](/docs/build/dimensions?dimension=time_gran)
 
 If you already have a date dimension or time-spine table in your dbt project, you can point MetricFlow to this table by updating the `model` configuration to use this table in the Semantic Layer. This is a model-level configuration that tells dbt to use the model for time range joins in the Semantic Layer. 
@@ -40,7 +44,7 @@ If you don’t have a date dimension table, you can still create one by using th
 <File name="models/_models.yml">
 
 ```yaml
-models:
+[models:](/reference/model-properties)
   - name: time_spine_hourly
     time_spine:
       standard_granularity_column: date_hour # column for the standard grain of your table
@@ -56,7 +60,7 @@ models:
 ```
 </File>
 
-For an example project, refer to our [Jaffle shop](https://github.com/dbt-labs/jaffle-sl-template/blob/main/models/marts/_models.yml) example.
+For an example project, refer to our [Jaffle shop](https://github.com/dbt-labs/jaffle-sl-template/blob/main/models/marts/_models.yml) example. Note that the [`models` key](/reference/model-properties) in the time spine configuration must be placed in your `models/` directory.
 
 Now, break down the configuration above. It's pointing to a model called `time_spine_daily`. It sets the time spine configurations under the `time_spine` key. The `standard_granularity_column` is the lowest grain of the table, in this case, it's hourly. It needs to reference a column defined under the columns key, in this case, `date_hour`. Use the `standard_granularity_column` as the join key for the time spine table when joining tables in MetricFlow. Here, the granularity of the `standard_granularity_column` is set at the column level, in this case, `hour`.
 

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

@@ -51,7 +51,10 @@ The dbt Cloud CLI offers additional flexibility by letting you choose the source
 <File name="dbt_cloud.yml">
 
   ```yml
-defer-env-id: '123456'
+context:
+  active-host: ...
+  active-project: ...
+  defer-env-id: '123456'
 ```
 
 </File>
@@ -60,7 +63,7 @@ defer-env-id: '123456'
 <File name="dbt_project.yml"> 
 
 ```yml
-dbt_cloud:
+dbt-cloud:
   defer-env-id: '123456'
 ```