[docs][dagster-dbt] use new with_insights chain method (#23203)

## Summary Adds docs for the new insights API introduced in #23183
dagster-io · Aug 6, 2024 · b63ebae · b63ebae · github-actions · Aug 6, 2024
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Showing 6 changed files with 15 additions and 35 deletions.
diff --git a/docs/content/api/modules.json.gz b/docs/content/api/modules.json.gz
diff --git a/docs/content/api/searchindex.json.gz b/docs/content/api/searchindex.json.gz
diff --git a/docs/content/api/sections.json.gz b/docs/content/api/sections.json.gz
diff --git a/docs/content/dagster-plus/insights/integrating-bigquery-and-dbt.mdx b/docs/content/dagster-plus/insights/integrating-bigquery-and-dbt.mdx
@@ -35,39 +35,29 @@ To complete the steps in this guide, you'll need:
 <TabGroup>
 <TabItem name="Using assets">
 
-First, instrument the Dagster <PyObject module="dagster_dbt" object="dbt_assets" decorator /> function with `dbt_with_bigquery_insights`:
+First, append <PyObject object="with_insights" module="dagster_dbt.core.dbt_cli_invocation.DbtEventIterator" displayText="with_insights()" /> to the dbt CLI call in your Dagster <PyObject module="dagster_dbt" object="dbt_assets" decorator /> function:
 
 ```python
-from dagster_cloud.dagster_insights import dbt_with_bigquery_insights
-
-
 @dbt_assets(...)
 def my_asset(context: AssetExecutionContext, dbt: DbtCliResource):
-    # Typically you have a `yield from dbt_resource.cli(...)`.
-    # Wrap the original call with `dbt_with_bigquery_insights` as below.
-    dbt_cli_invocation = dbt_resource.cli(["build"], context=context)
-    yield from dbt_with_bigquery_insights(context, dbt_cli_invocation)
+    # Chain `with_insights` after any other metadata fetch, e.g. `fetch_row_count`
+    yield from dbt_resource.cli(["build"], context=context).stream().with_insights()
 ```
 
 This passes through all underlying events and emits additional <PyObject object="AssetObservation" pluralize /> with BigQuery cost metrics. These metrics are obtained by querying the underlying `INFORMATION_SCHEMA.JOBS` table, using the BigQuery client from the dbt adapter.
 
 </TabItem>
 <TabItem name="Using ops and jobs">
 
-First, instrument the op function with `dbt_with_bigquery_insights`:
+First, append <PyObject object="with_insights" module="dagster_dbt.core.dbt_cli_invocation.DbtEventIterator" displayText="with_insights()" /> to the dbt CLI call in your Dagster op function:
 
 ```python
-from dagster_cloud.dagster_insights import dbt_with_bigquery_insights
-
-
 @op(out={})
 def my_dbt_op(context: OpExecutionContext, dbt: DbtCliResource):
-    # Typically you have a `yield from dbt_resource.cli(...)`.
-    # Wrap the original call with `dbt_with_bigquery_insights` as below.
-    dbt_cli_invocation = dbt.cli(
+    # Chain `with_insights` after any other metadata fetch, e.g. `fetch_row_count`
+    yield from  dbt.cli(
         ["build"], context=context, manifest=dbt_manifest_path
-    )
-    yield from dbt_with_bigquery_insights(context, dbt_cli_invocation)
+    ).stream().with_insights()
 
 @job
 def my_dbt_job():
@@ -110,7 +100,7 @@ width={3454}
 height={1338}
 />
 
-The BigQuery cost metric is based off of the bytes billed for queries wrapped with `dbt_with_bigquery_insights`, based on a unit price of $6.25 USD per TiB.
+The BigQuery cost metric is based off of the bytes billed for queries wrapped with `with_insights`, based on a unit price of $6.25 USD per TiB.
 
 ---
 

diff --git a/docs/content/dagster-plus/insights/integrating-snowflake-and-dbt.mdx b/docs/content/dagster-plus/insights/integrating-snowflake-and-dbt.mdx
@@ -35,39 +35,29 @@ To complete the steps in this guide, you'll need:
 <TabGroup>
 <TabItem name="Using assets">
 
-First, instrument the Dagster <PyObject module="dagster_dbt" object="dbt_assets" decorator /> function with `dbt_with_snowflake_insights`:
+First, append <PyObject object="with_insights" module="dagster_dbt.core.dbt_cli_invocation.DbtEventIterator" displayText="with_insights()" /> to the dbt CLI call in your Dagster <PyObject module="dagster_dbt" object="dbt_assets" decorator /> function:
 
 ```python
-from dagster_cloud.dagster_insights import dbt_with_snowflake_insights
-
-
 @dbt_assets(...)
 def my_asset(context: AssetExecutionContext, dbt: DbtCliResource):
-    # Typically you have a `yield from dbt_resource.cli(...)`.
-    # Wrap the original call with `dbt_with_snowflake_insights` as below.
-    dbt_cli_invocation = dbt_resource.cli(["build"], context=context)
-    yield from dbt_with_snowflake_insights(context, dbt_cli_invocation)
+    # Chain `with_insights` after any other metadata fetch, e.g. `fetch_row_count`
+    yield from dbt_resource.cli(["build"], context=context).stream().with_insights()
 ```
 
 This passes through all underlying events and emits an <PyObject object="AssetObservation" /> for each asset materialization. The observation contains the dbt invocation ID and unique ID recorded in the Dagster event log.
 
 </TabItem>
 <TabItem name="Using ops and jobs">
 
-First, instrument the op function with `dbt_with_snowflake_insights`:
+First, append <PyObject object="with_insights" module="dagster_dbt.core.dbt_cli_invocation.DbtEventIterator" displayText="with_insights()" /> to the dbt CLI call in your Dagster op function:
 
 ```python
-from dagster_cloud.dagster_insights import dbt_with_snowflake_insights
-
-
 @op(out={})
 def my_dbt_op(context: OpExecutionContext, dbt: DbtCliResource):
-    # Typically you have a `yield from dbt_resource.cli(...)`.
-    # Wrap the original call with `dbt_with_snowflake_insights` as below.
-    dbt_cli_invocation = dbt.cli(
+    # Chain `with_insights` after any other metadata fetch, e.g. `fetch_row_count`
+    yield from  dbt.cli(
         ["build"], context=context, manifest=dbt_manifest_path
-    )
-    yield from dbt_with_snowflake_insights(context, dbt_cli_invocation)
+    ).stream().with_insights()
 
 @job
 def my_dbt_job():

diff --git a/docs/next/public/objects.inv b/docs/next/public/objects.inv