Update docs based on code PR #1

docs/_api-guide-template.md

@@ -1,7 +1,7 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-# [Feature Name]
+# Include Project and Model Names in Looker Explores
 
 
 <!-- This section should provide plain-language explanations on the feature itself, preferably in one paragraph, and should link to the feature guide for more detailed information.
@@ -10,7 +10,7 @@ import TabItem from '@theme/TabItem';
 
 ### Goal of This Guide
 
-This guide will show you how to...
+This guide will show you how to include project and model names in Looker explores' custom properties during data ingestion.
 
 <!-- This should include bullet points of the main goals(actions) for this tutorial, such as
 - Add lineage between datasets
@@ -29,7 +29,7 @@ Before adding lineage, you need to ensure the targeted dataset is already presen
 :::
 -->
 
-## [Action] [Feature Name]
+## Add Project and Model Names to Looker Explores
 <!-- The heading should short and straightforward, and start with verb and end with the feature name. For example, "Add Lineage" 
 Each action should have the same format of code snippets (Tabs) and expected outcomes. -->
 

docs/api/restli/restli-overview.md

@@ -1018,6 +1018,8 @@ curl -X POST 'http://localhost:8080/entities?action=browse' \
 }
 ```
 
+Note: Looker entities now include additional custom properties such as "project" and "model" which can be useful for more detailed metadata queries.
+
 ##### Search an Entity
 
 To search for an Entity of a particular type (e.g. dataset, chart, etc), you can use the following query format: 
@@ -1080,6 +1082,8 @@ curl -X POST 'http://localhost:8080/entities?action=search' \
 }
 ```
 
+Note: You can now search for Looker entities using the new custom properties "project" and "model" to refine your search results.
+
 ###### Exact Match Search
 
 You can use colon search for exact match searching on particular @Searchable fields of an Entity. 

docs/api/tutorials/custom-properties.md

@@ -30,7 +30,7 @@ If you attempt to manipulate entities that do not exist, your operation will fai
 In this guide, we will be using data from sample ingestion.
 :::
 
-In this example, we will add some custom properties `cluster_name` and `retention_time` to the dataset `fct_users_deleted`.
+In this example, we will add some custom properties `cluster_name`, `retention_time`, `project`, and `model` to the dataset `fct_users_deleted`.
 
 After you have ingested sample data, the dataset `fct_users_deleted` should have a custom properties section with `encoding` set to `utf-8`.
 
@@ -55,7 +55,7 @@ datahub get --urn "urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PR
 
 ## Add Custom Properties programmatically
 
-The following code adds custom properties `cluster_name` and `retention_time` to a dataset named `fct_users_deleted` without affecting existing properties.
+The following code adds custom properties `cluster_name`, `retention_time`, `project`, and `model` to a dataset named `fct_users_deleted` without affecting existing properties.
 
 <Tabs>
 <TabItem value="graphql" label="GraphQL">
@@ -82,7 +82,7 @@ The following code adds custom properties `cluster_name` and `retention_time` to
 
 ### Expected Outcome of Adding Custom Properties
 
-You can now see the two new properties are added to `fct_users_deleted` and the previous property `encoding` is unchanged.
+You can now see the four new properties are added to `fct_users_deleted` and the previous property `encoding` is unchanged.
 
 
 <p align="center">

docs/modeling/metadata-model.md

@@ -59,7 +59,7 @@ Traditionally, the Entity Registry was constructed using [Snapshot](https://gith
 an Entity to the Aspects associated with it. An example is [DatasetSnapshot](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/metadata/snapshot/DatasetSnapshot.pdl), which defines the core `Dataset` Entity. 
 The Aspects of the Dataset entity are captured via a union field inside a special "Aspect" schema. An example is 
 [DatasetAspect](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/metadata/aspect/DatasetAspect.pdl).
-This file associates dataset-specific aspects (like [DatasetProperties](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetProperties.pdl)) and common aspects  (like [Ownership](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Ownership.pdl), 
+This file associates dataset-specific aspects (like [DatasetProperties](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetProperties.pdl), which now includes 'project' and 'model' properties for Looker explores) and common aspects  (like [Ownership](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Ownership.pdl), 
 [InstitutionalMemory](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/InstitutionalMemory.pdl), 
 and [Status](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Status.pdl)) 
 to the Dataset Entity. This approach to defining Entities will soon be deprecated in favor of a new approach. 

docs/quick-ingestion-guides/looker/configuration.md

@@ -62,6 +62,8 @@ Enter the details into the Looker Recipe.
 * **Base URL:** This is your looker instance URL. (i.e. `https://<your-looker-instance>.cloud.looker.com`)
 * **Client ID:** Use the secret LOOKER_CLIENT_ID with the format `${LOOKER_CLIENT_ID}`.
 * **Client Secret:** Use the secret LOOKER_CLIENT_SECRET with the format `${LOOKER_CLIENT_SECRET}`.
+* **Project Name:** Optionally specify the project name to include in the custom properties.
+* **Model Name:** Optionally specify the model name to include in the custom properties.
 
 
 Optionally, use the `dashboard_pattern` and `chart_pattern` fields to filter for specific dashboard and chart.

docs/quick-ingestion-guides/looker/overview.md

@@ -18,6 +18,8 @@ Looker connector will ingest Looker asset types:
 * [Schemas](https://developers.looker.com/api/explorer/4.0/methods/Metadata/connection_schemas) 
 * [Owners of Dashboards](https://cloud.google.com/looker/docs/creating-user-defined-dashboards)
 
+Additionally, the Looker connector will now include 'project' and 'model' properties in the metadata for explores.
+
 :::note
 
 To get complete Looker metadata integration (including Looker views and lineage to the underlying warehouse tables), you must also use the [lookml](https://datahubproject.io/docs/generated/ingestion/sources/looker#module-lookml) connector.