Merge branch 'current' into mwong-sql

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

@@ -37,24 +37,6 @@ MetricFlow abides by these principles:
 - **Simplicity with gradual complexity:** Approach MetricFlow using familiar data modeling concepts.
 - **Performance and efficiency**: Optimize performance while supporting centralized data engineering and distributed logic ownership.
 
-<!--MetricFlow is a SQL query generation engine that helps you create metrics by constructing appropriate queries for different granularities and dimensions that are useful for various business applications. 
-
-- It uses YAML files to define a semantic graph, which maps language to data. This graph consists of , which serve as data entry points, and [, which are functions used to create new quantitative indicators.
-
-- MetricFlow is a  () and available on dbt versions 1.6 and higher. 
-
-- 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.
-
-- To query metrics dimensions, dimension values, and validate your configurations; install MetricFlow in 
-
-MetricFlow has the following principles:
-
-- **Flexible, but complete** &mdash;  Ability to create any metric on any data model by defining logic in flexible abstractions.
-- **Don't Repeat Yourself (DRY)** &mdash; Avoid repetition by allowing metric definitions to be enabled whenever possible.
-- **Simple with progressive complexity** &mdash; Make MetricFlow approachable by relying on known concepts and structures in data modeling. 
-- **Performant and efficient** &mdash; Allow for performance optimizations in centralized data engineering while still enabling distributed definition and ownership of logic.
--->
-
 ### Semantic graph 
 
 We're introducing a new concept: a "semantic graph". It's the relationship between semantic models and YAML configurations that creates a data landscape for building metrics. You can think of it like a map, where tables are like locations, and the connections between them (edges) are like roads. Although it's under the hood, the semantic graph is a subset of the <Term id="dag" />, and you can see the semantic models as nodes on the DAG.
@@ -73,6 +55,8 @@ For a semantic model, there are three main pieces of metadata:
 * [Dimensions](/docs/build/dimensions) &mdash; These are the ways you want to group or slice/dice your metrics.
 * [Measures](/docs/build/measures) &mdash; The aggregation functions that give you a numeric result and can be used to create your metrics.
 
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/semantic_foundation.jpg" width="70%" title="A semantic model is made up of different components: Entities, Measures, and Dimensions."/>
+
 ### Metrics 
 
 Metrics, which is a key concept, are functions that combine measures, constraints, or other mathematical functions to define new quantitative indicators. MetricFlow uses measures and various aggregation types, such as average, sum, and count distinct, to create metrics.  Dimensions add context to metrics and without them, a metric is simply a number for all time. You can define metrics in the same YAML files as your semantic models, or create a new file.

website/docs/docs/build/incremental-models.md

@@ -390,7 +390,7 @@ models:
       # `DBT_INTERNAL_DEST` and `DBT_INTERNAL_SOURCE` are the standard aliases for the target table and temporary table, respectively, during an incremental run using the merge strategy. 
 ```
 
-Alternatively, here are the same same configurations configured within a model file:
+Alternatively, here are the same configurations configured within a model file:
 
 ```sql
 -- in models/my_incremental_model.sql

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

@@ -6,11 +6,11 @@ sidebar_label: "MetricFlow commands"
 tags: [Metrics, Semantic Layer]
 ---
 
-Once you define metrics in your dbt project, you can query metrics, dimensions, dimension values, and validate your configs using the MetricFlow commands. 
+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 is compatible with Python versions 3.8, 3.9, 3.10 and 3.11.
+MetricFlow is compatible with Python versions 3.8, 3.9, 3.10, and 3.11.
 
 
 ## MetricFlow
@@ -25,7 +25,7 @@ MetricFlow is a dbt package that allows you to define and query metrics in your
 
 MetricFlow commands are embedded in the dbt Cloud CLI, which means you can immediately run them once you install the dbt Cloud CLI. 
 
-A benefit to using the dbt Cloud CLI or dbt Cloud IDE is that you won't need to manage versioning — your dbt Cloud account will automatically manage the versioning for you.
+A benefit to using the dbt Cloud is that you won't need to manage versioning — your dbt Cloud account will automatically manage the versioning.
 
 </TabItem>
 
@@ -35,6 +35,8 @@ A benefit to using the dbt Cloud CLI or dbt Cloud IDE is that you won't need to
 You can create metrics using MetricFlow in the dbt Cloud IDE. However, support for running MetricFlow commands in the IDE will be available soon.
 :::
 
+A benefit to using the dbt Cloud is that you won't need to manage versioning &mdash; your dbt Cloud account will automatically manage the versioning.
+
 </TabItem>
 
 <TabItem value="core" label="dbt Core">
@@ -44,13 +46,13 @@ You can create metrics using MetricFlow in the dbt Cloud IDE. However, support f
 
 Use the dbt Cloud CLI for the experience in defining and querying metrics in your dbt project on dbt Cloud or dbt Core with MetricFlow. 
 
-A benefit to using the dbt Cloud CLI or dbt Cloud IDE is that you won't need to manage versioning — your dbt Cloud account will automatically manage the versioning for you.
+A benefit to using the dbt Cloud is that you won't need to manage versioning &mdash; your dbt Cloud account will automatically manage the versioning.
 :::
 
 
-You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-started) from [PyPI](https://pypi.org/project/dbt-metricflow/). You need to use `pip` to instal MetricFlow on Windows or Linux operating systems:
+You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-started) from [PyPI](https://pypi.org/project/dbt-metricflow/). You need to use `pip` to install MetricFlow on Windows or Linux operating systems:
 
-1. Create or activate your virtual environment`python -m venv venv`
+1. Create or activate your virtual environment `python -m venv venv`
 2. Run `pip install dbt-metricflow`
   * You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. 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]"`
 
@@ -60,6 +62,7 @@ You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-star
 
 </Tabs>
 
+Something to note, MetricFlow `mf` commands return an error if you have a Metafont latex package installed. To run `mf` commands, uninstall the package.
 
 ## MetricFlow commands
 
@@ -77,7 +80,7 @@ Use the `dbt sl` prefix before the command name to execute them in dbt Cloud. Fo
 - [`list entities`](#list-entities) &mdash; Lists all unique entities.
 - [`query`](#query) &mdash; Query metrics and dimensions you want to see in the command line interface. Refer to [query examples](#query-examples) to help you get started.
 
-<!--below commands aren't support in dbt cloud yet
+<!--below commands aren't supported in dbt cloud yet
 - [`validate-configs`](#validate-configs) &mdash; Validates semantic model configurations.
 - [`health-checks`](#health-checks) &mdash; Performs data platform health check.
 - [`tutorial`](#tutorial) &mdash; Dedicated MetricFlow tutorial to help get you started.
@@ -552,3 +555,4 @@ Keep in mind that modifying your shell configuration files can have an impact on
 </details>
 
 </details>
+

website/docs/docs/build/semantic-models.md

@@ -18,6 +18,8 @@ Semantic models are the foundation for data definition in MetricFlow, which powe
 - Configure semantic models in a YAML file within your dbt project directory.
 - Organize them under a `metrics:` folder or within project sources as needed. 
 
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/semantic_foundation.jpg" width="70%" title="A semantic model is made up of different components: Entities, Measures, and Dimensions."/>
+
 Semantic models have 6 components and this page explains the definitions with some examples:
 
 | Component | Description | Type |

website/docs/docs/build/sl-getting-started.md

@@ -94,3 +94,4 @@ The dbt Semantic Layer is proprietary, however, some components of the dbt Seman
 - [Build your metrics](/docs/build/build-metrics-intro)
 - [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
 - [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
+- Demo on [how to define and query metrics with MetricFlow](https://www.loom.com/share/60a76f6034b0441788d73638808e92ac?sid=861a94ac-25eb-4fd8-a310-58e159950f5a)