Refining Metadata Model docs further

docs/modeling/metadata-model.md

@@ -45,18 +45,18 @@ DataHub's "core" Entity types model the Data Assets that comprise the Modern Dat
 
 ## The Entity Registry
 
-Where are Entities and their aspects defined in DataHub? Where doe the Metadata Model "live"? The Metadata Model is "stitched together" by means 
+Where are Entities and their aspects defined in DataHub? Where does the Metadata Model "live"? The Metadata Model is stitched together by means 
 of an **Entity Registry**, a catalog of Entities that comprise the Metadata Graph along with the aspects associated with each. Put
-simply, this is where the "schema" of the model is represented. 
+simply, this is where the "schema" of the model is defined. 
 
-Traditionally, the Entity Registry was constructed using [Snapshot](https://github.com/linkedin/datahub/tree/master/metadata-models/src/main/pegasus/com/linkedin/metadata/snapshot) models, which Data Models (schemas) that explicitly tied 
-an Entity to the Aspects associated with it. An example is [DatasetSnapshot](https://github.com/linkedin/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/metadata/snapshot/DatasetSnapshot.pdl), which declares the core `Dataset` Entity. 
-The relationship particular aspects of a Dataset was captured via a "union" where the list of possible aspects was modeled. An example is 
+Traditionally, the Entity Registry was constructed using [Snapshot](https://github.com/linkedin/datahub/tree/master/metadata-models/src/main/pegasus/com/linkedin/metadata/snapshot) models, which are schemas that explicitly tie 
+an Entity to the Aspects associated with it. An example is [DatasetSnapshot](https://github.com/linkedin/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/linkedin/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/metadata/aspect/DatasetAspect.pdl).
-This file associates dataset-specific aspects, such as [DatasetProperties](https://github.com/linkedin/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetProperties.pdl), as well as common aspects like [Ownership](https://github.com/linkedin/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Ownership.pdl), 
+This file associates dataset-specific aspects (like [DatasetProperties](https://github.com/linkedin/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetProperties.pdl)) and common aspects  (like [Ownership](https://github.com/linkedin/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Ownership.pdl), 
 [InstitutionalMemory](https://github.com/linkedin/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/InstitutionalMemory.pdl), 
-and [Status](https://github.com/linkedin/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Status.pdl), 
-to the Dataset Entity. Similar Snapshots 
+and [Status](https://github.com/linkedin/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. 
 
 As of January 2022, DataHub has deprecated support for Snapshot models as a means of adding new entities. Instead,
 the Entity Registry is defined inside a YAML configuration file called [entity-registry.yml](https://github.com/linkedin/datahub/blob/master/metadata-models/src/main/resources/entity-registry.yml),
@@ -67,7 +67,6 @@ each aspect name provided by configuration (via the [@Aspect](https://github.com
 By moving to this format, evolving the Metadata Model becomes much easier. Adding Entities & Aspects becomes a matter of adding a 
 to the YAML configuration, instead of creating new Snapshot / Aspect files. 
 
-> New to [PDL](https://linkedin.github.io/rest.li/pdl_schema) files? Don't fret. They are just a way to define a JSON document "schema" for Aspects in DataHub. All Data ingested to DataHub's Metadata Service is validated against a PDL schema, with each @Aspect corresponding to a single schema. Structurally, PDL is quite similar to [Protobuf](https://developers.google.com/protocol-buffers) and conveniently maps to JSON. 
 
 ## Exploring DataHub's Metadata Model
 
@@ -104,6 +103,8 @@ DataHub’s modeling language allows you to optimize metadata persistence to ali
 
 There are three supported ways to query the metadata graph: by primary key lookup, a search query, and via relationship traversal. 
 
+> New to [PDL](https://linkedin.github.io/rest.li/pdl_schema) files? Don't fret. They are just a way to define a JSON document "schema" for Aspects in DataHub. All Data ingested to DataHub's Metadata Service is validated against a PDL schema, with each @Aspect corresponding to a single schema. Structurally, PDL is quite similar to [Protobuf](https://developers.google.com/protocol-buffers) and conveniently maps to JSON.
+
 ### Querying an Entity
 
 #### Fetching Latest Entity Aspects (Snapshot)

metadata-ingestion/src/datahub/ingestion/source/identity/azure_ad.py

@@ -194,7 +194,7 @@ def get_workunits(self) -> Iterable[MetadataWorkUnit]:
                 datahub_corp_user_snapshots, datahub_corp_user_urn_to_group_membership
             )
 
-        # Create MetadatWorkUnits for CorpUsers
+        # Create MetadataWorkUnits for CorpUsers
         if self.config.ingest_users:
             # 3) the users
             for azure_ad_users in self._get_azure_ad_users():

smoke-test/tests/domains/domains_test.py

@@ -5,6 +5,18 @@
 from tests.utils import ingest_file_via_rest
 from tests.utils import delete_urns_from_file
 
+from typing import List
+
+import datahub.emitter.mce_builder as builder
+from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+from datahub.metadata.com.linkedin.pegasus2avro.dataset import (
+    DatasetLineageTypeClass,
+    UpstreamClass,
+    UpstreamLineage,
+)
+from datahub.metadata.schema_classes import ChangeTypeClass
+
 @pytest.fixture(scope="module", autouse=False)
 def ingest_cleanup_data(request):
     print("ingesting domains test data")
@@ -21,6 +33,39 @@ def test_healthchecks(wait_for_healthchecks):
 @pytest.mark.dependency(depends=["test_healthchecks"])
 def test_create_list_get_domain(frontend_session):
 
+    # Construct upstream tables.
+    upstream_tables: List[UpstreamClass] = []
+    upstream_table_1 = UpstreamClass(
+        dataset=builder.make_dataset_urn("bigquery", "upstream_table_1", "PROD"),
+        type=DatasetLineageTypeClass.TRANSFORMED,
+    )
+    upstream_tables.append(upstream_table_1)
+    upstream_table_2 = UpstreamClass(
+        dataset=builder.make_dataset_urn("bigquery", "upstream_table_2", "PROD"),
+        type=DatasetLineageTypeClass.TRANSFORMED,
+    )
+    upstream_tables.append(upstream_table_2)
+
+    # Construct a lineage object.
+    upstream_lineage = UpstreamLineage(upstreams=upstream_tables)
+
+    # Construct a MetadataChangeProposalWrapper object.
+    lineage_mcp = MetadataChangeProposalWrapper(
+        entityType="dataset",
+        changeType=ChangeTypeClass.UPSERT,
+        entityUrn=builder.make_dataset_urn("bigquery", "downstream"),
+        aspectName="upstreamLineage",
+        aspect=upstream_lineage,
+        systemMetadata=
+    )
+
+    # Create an emitter to the GMS REST API.
+    emitter = DatahubRestEmitter("http://localhost:8080")
+
+    # Emit metadata!
+    emitter.emit_mcp(lineage_mcp)
+
+
     # Get count of existing secrets
     list_domains_json = {
         "query": """query listDomains($input: ListDomainsInput!) {\n