dbt integration front page edits (#14937)

## Summary & Motivation

This is part of a larger set of software + docs changes for making
Dagster more accessible to dbt users.

This makes some edits to the front page for the dbt integration, with a
few higher-level aims:
- Get the value prop across ASAP
- Limit Dagster-specific terminology early on that might feel
overwhelming
- Support different learning styles

Specific changes:
- Near the top, added an image & code sample of a dbt graph loaded into
Dagster.
- Made some tweaks to the intro text based on my understanding of what
language will get across best for learning Dagster + dbt users.
- Present the tutorial as one of a few different options for getting
started with Dagster & dbt. When the dbt-focused Cloud NUX is ready, we
can add that as another option.
- Added a link to a Dagster+dbt example project.
- Moved the section on how Dagster assets relate to dbt models farther
down.

## How I Tested These Changes

docs/content/integrations/dbt.mdx

@@ -10,69 +10,56 @@ description: Dagster can orchestrate dbt alongside other technologies.
   <a href="/integrations/dbt-cloud">dbt Cloud with Dagster guide</a>!
 </Note>
 
-Dagster orchestrates dbt alongside other technologies, so you can combine dbt with Spark, Python, etc. in a single workflow. Dagster's [software-defined asset](/concepts/assets/software-defined-assets) abstractions make it simple to define data assets that depend on specific dbt models, or to define the computation required to compute the sources that your dbt models depend on. You could, for example:
+Dagster orchestrates dbt alongside other technologies, so you can schedule dbt with Spark, Python, etc. in a single data pipeline.
 
-- Run your dbt models after ingesting data into your data warehouse
-- Selectively materialize dbt models and their dependencies
+Dagster's [Software-defined Asset](/concepts/assets/software-defined-assets) approach allows Dagster to understand dbt at the level of individual dbt models. This means that you can:
 
-Dagster has built-in support for loading dbt models, seeds, and snapshots as software-defined assets, enabling you to:
+- Use Dagster's UI or APIs to run subsets of your dbt models, seeds, and snapshots.
+- Track failures, logs, and run history for individual dbt models, seeds, and snapshots.
+- Define dependencies between individual dbt models and other data assets. For example, put dbt models after the Fivetran-ingested table that they read from, or put a machine learning after the dbt models that it's trained from.
 
-- Visualize and orchestrate a graph of dbt assets, and execute them with a single dbt invocation
-- Version your dbt models by their defining SQL code, allowing Dagster to indicate when a model has changed
-- View detailed historical metadata and logs for each asset
-- Define Python computations that depend directly on tables updated using dbt
-- Track data lineage through dbt and your other tools
+An asset graph like this:
 
----
+<!-- ![Dagster graph with dbt, Fivetran, and TensorFlow](/images/integrations/dbt/dagster-dbt-fivetran-tensorflow.png) -->
 
-## Using dbt with Dagster
+<Image
+alt="Dagster graph with dbt, Fivetran, and TensorFlow"
+src="/images/integrations/dbt/dagster-dbt-fivetran-tensorflow.png"
+width={1834}
+height={1220}
+/>
+
+Can be produced from code like this:
+
+```python file=/integrations/dbt/potemkin_dag_for_cover_image.py startafter=start endbefore=end
+fivetran_assets = dagster_fivetran.build_fivetran_assets(
+    connector_id="postgres",
+    table_names=["users", "orders"],
+)
+
+dbt_assets = dagster_dbt.load_assets_from_dbt_manifest("manifest.json")
 
-<DbtModelAssetExplanation />
 
-To learn how to load dbt models into Dagster as assets, check out the tutorial below or the quick version in the [dagster-dbt reference](/integrations/dbt/reference#loading-dbt-models-from-a-dbt-project).
+@asset(compute_kind="tensorflow", non_argument_deps={"daily_order_summary"})
+def predicted_orders():
+    ...
+```
 
 ---
 
-## dbt and Dagster software-defined assets tutorial
-
-In this tutorial, we'll walk you through integrating dbt with Dagster using dbt's example [jaffle shop project](https://github.com/dbt-labs/jaffle_shop), the [dagster-dbt library](/\_apidocs/libraries/dagster-dbt), and a data warehouse, such as [DuckDB](https://duckdb.org/).
-
-By the end of the tutorial, you'll have a working dbt and Dagster integration and a handful of materialized Dagster assets, including a plotly chart powered by data computed from your dbt models.
-
-<ArticleList>
-  <ArticleListItem
-    title="Overview and prerequisites"
-    href="/integrations/dbt/using-dbt-with-dagster"
-  ></ArticleListItem>
-  <ArticleListItem
-    title="Part one: Set up a dbt project"
-    href="/integrations/dbt/using-dbt-with-dagster/part-one"
-  ></ArticleListItem>
-  <ArticleListItem
-    title="Part two: Load dbt models as Dagster assets"
-    href="/integrations/dbt/using-dbt-with-dagster/part-two"
-  ></ArticleListItem>
-  <ArticleListItem
-    title="Part three: Create and materialize upstream Dagster assets"
-    href="/integrations/dbt/using-dbt-with-dagster/part-three"
-  ></ArticleListItem>
-  <ArticleListItem
-    title="Part four: Create and materialize a downstream asset"
-    href="/integrations/dbt/using-dbt-with-dagster/part-four"
-  ></ArticleListItem>
-</ArticleList>
+## Getting started
+
+There are a few ways to get started with Dagster and dbt:
+
+- Take the [tutorial](/integrations/dbt/using-dbt-with-dagster). We'll walk you through setting up dbt and Dagster together on your computer, using dbt's example [jaffle shop project](https://github.com/dbt-labs/jaffle_shop), the [dagster-dbt library](/\_apidocs/libraries/dagster-dbt), and a data warehouse, such as [DuckDB](https://duckdb.org/). By the end, you'll have a working dbt and Dagster project and a handful of materialized Dagster assets, including a chart powered by data from your dbt models.
+- Play around with a [working dbt + Dagster project](https://github.com/dagster-io/dagster/tree/master/examples/assets_dbt_python).
+- Browse the [dagster-dbt integration reference](/integrations/dbt/reference) for short lessons on Dagster + dbt topics.
+- Review the [API docs](/\_apidocs/libraries/dagster-dbt) for the dagster-dbt library.
 
 ---
 
-## References
-
-<ArticleList>
-  <ArticleListItem
-    title="dagster-dbt integration reference"
-    href="/integrations/dbt/reference"
-  ></ArticleListItem>
-  <ArticleListItem
-    title="dagster-dbt API reference"
-    href="/\_apidocs/libraries/dagster-dbt"
-  ></ArticleListItem>
-</ArticleList>
+## Understanding how dbt models relate to Dagster Software-defined assets
+
+<DbtModelAssetExplanation />
+
+To learn how to load dbt models into Dagster as assets, check out the [tutorial](/integrations/dbt/using-dbt-with-dagster) or the quick version in the [dagster-dbt reference](/integrations/dbt/reference#loading-dbt-models-from-a-dbt-project).

examples/assets_dbt_python/README.md

@@ -14,6 +14,12 @@ Check out [Dagster Cloud](https://dagster.io/cloud) to get started.
 
 ### Option 2: Running it locally
 
+To download this example into your working directory, run:
+
+```bash
+dagster project from-example --example assets_dbt_python --name assets_dbt_python
+```
+
 To install this example and its Python dependencies, run:
 
 ```bash

examples/docs_snippets/docs_snippets/integrations/dbt/potemkin_dag_for_cover_image.py

@@ -0,0 +1,59 @@
+"""This is used to generate the image on code snippet on the dbt front page.
+
+We pull off some dark magic so that generating the screenshot doesn't involve a whole setup with
+Fivetran and a database.
+"""
+
+from dagster import asset
+
+
+class dagster_fivetran:
+    @staticmethod
+    def build_fivetran_assets(connector_id, table_names):
+        @asset(compute_kind="fivetran")
+        def users():
+            ...
+
+        @asset(compute_kind="fivetran")
+        def orders():
+            ...
+
+        return [users, orders]
+
+
+class dagster_dbt:
+    @staticmethod
+    def load_assets_from_dbt_manifest(manifest):
+        @asset(non_argument_deps={"users"}, compute_kind="dbt")
+        def stg_users():
+            """Users with test accounts removed."""
+            ...
+
+        @asset(non_argument_deps={"orders"}, compute_kind="dbt")
+        def stg_orders():
+            """Cleaned orders table."""
+            ...
+
+        @asset(non_argument_deps={"stg_users", "stg_orders"}, compute_kind="dbt")
+        def daily_order_summary():
+            """Summary of daily orders, by user."""
+            raise ValueError()
+
+        return [stg_users, stg_orders, daily_order_summary]
+
+
+# start
+fivetran_assets = dagster_fivetran.build_fivetran_assets(
+    connector_id="postgres",
+    table_names=["users", "orders"],
+)
+
+dbt_assets = dagster_dbt.load_assets_from_dbt_manifest("manifest.json")
+
+
+@asset(compute_kind="tensorflow", non_argument_deps={"daily_order_summary"})
+def predicted_orders():
+    ...
+
+
+# end