Merge branch 'current' into mwong-sl-beta

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

@@ -33,7 +33,9 @@ There are a few key principles:
 
 - MetricFlow, as a part of the dbt Semantic Layer, allows organizations to define company metrics logic through YAML abstractions, as described in the following sections.
 
-- You can install MetricFlow via PyPI as an extension of your [dbt adapter](/docs/supported-data-platforms) in the CLI. To install the adapter, run `pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `pip install "dbt-metricflow[snowflake]"`.
+- You can install MetricFlow using PyPI as an extension of your [dbt adapter](/docs/supported-data-platforms) in the CLI. To install the adapter, run `pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `pip install "dbt-metricflow[snowflake]"`.
+
+- To query metrics dimensions, dimension values, and validate your configurations; install the [MetricFlow CLI](/docs/build/metricflow-cli).
 
 ### Semantic graph 
 

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

@@ -18,11 +18,13 @@ To fully experience the dbt Semantic Layer, including the ability to query dbt m
 :::
 
 Before you start, keep the following considerations in mind:
+
 - Define metrics in YAML and query them using the [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
 - You must be on dbt v1.6 or higher to use MetricFlow. [Upgrade your dbt Cloud version](/docs/dbt-versions/upgrade-core-in-cloud) to get started.
 - MetricFlow currently supports Snowflake, Postgres, BigQuery, Databricks, and Redshift.
 - Unlock insights and query your metrics using the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and its diverse range of [available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).
 
+
 <div className="grid--3-col">
 
 <Card

website/docs/docs/build/measures.md

@@ -15,6 +15,27 @@ Measures are aggregations performed on columns in your model. They can be used a
 | [`agg`](#aggregation) | dbt supports the following aggregations: `sum`, `max`, `min`, `count_distinct`, and `sum_boolean`. | Required |
 | [`expr`](#expr) | You can either reference an existing column in the table or use a SQL expression to create or derive a new one. | Optional |
 | [`non_additive_dimension`](#non-additive-dimensions) | Non-additive dimensions can be specified for measures that cannot be aggregated over certain dimensions, such as bank account balances, to avoid producing incorrect results. | Optional |
+| [`agg_params`] | specific aggregation properties such as a percentile. | [Optional]|
+| [`agg_time_dimension`] | The time field. Defaults to the default agg time dimension for the semantic model.  | [Optional] |
+| [`non_additive_dimension`] | Use these configs when you need non-additive dimensions. | [Optional]|
+| [`label`] | How the metric appears in project docs and downstream integrations. | [Required]|
+
+
+## Measure spec
+
+An example of the complete YAML measures spec is below. The actual configuration of your measures will depend on the aggregation you're using.
+
+```bash
+measures:
+      - name: The name of the measure # think transaction_total. If `expr` isn't present then this is the expected name of the column [Required]
+        description: same as always [Optional]
+        agg: the aggregation type.  #think average, sum, max, min, etc.[Required]
+        expr: the field # think transaction_total or some other name you might want to alias [Optional]
+        agg_params: specific aggregation properties such as a percentile [Optional]
+        agg_time_dimension: The time field. Defaults to the default agg time dimension for the semantic model. [Optional]
+        non_additive_dimension: Use these configs when you need non-additive dimensions. [Optional]
+        label: How the metric appears in project docs and downstream integrations. [Required]
+```
 
 ### Name 
 
@@ -62,7 +83,7 @@ If you use the `dayofweek` function in the `expr` parameter with the legacy Snow
 ```yaml
 semantic_models:
  - name: transactions
-    description: A record for every transaction that takes place. Carts are considered  multiple transactions for each SKU.
+    description: A record of every transaction that takes place. Carts are considered  multiple transactions for each SKU.
     model: ref('schema.transactions')
     defaults:
       agg_time_dimensions:
@@ -190,14 +211,14 @@ semantic_models:
           name: metric_time
           window_choice: min 
       - name: mrr_end_of_month
-        description: Aggregate by summing all users active subscription plans at end of month 
+        description: Aggregate by summing all users' active subscription plans at the end of month 
         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 users MRR at the end of the month 
+        description: Group by user_id to achieve each user's MRR at the end of the month 
         expr: subscription_value
         agg: sum  
         non_additive_dimension: