diff --git a/metadata-ingestion/docs/sources/kafka/kafka.md b/metadata-ingestion/docs/sources/kafka/kafka.md
index 9fdfc3a3af1d0..f9b50c13f0277 100644
--- a/metadata-ingestion/docs/sources/kafka/kafka.md
+++ b/metadata-ingestion/docs/sources/kafka/kafka.md
@@ -4,9 +4,10 @@ Stateful Ingestion is available only when a Platform Instance is assigned to thi
 
 ### Connecting to Confluent Cloud
 
-If using Confluent Cloud you can use a recipe like this. In this `consumer_config.sasl.username` and `consumer_config.sasl.password` are the API credentials that you get (in the Confluent UI) from your cluster -> Data Integration -> API Keys. `schema_registry_config.basic.auth.user.info`  has API credentials for Confluent schema registry which you get (in Confluent UI) from Schema Registry -> API credentials.
+If using Confluent Cloud you can use a recipe like this. In this `consumer_config.sasl.username` and `consumer_config.sasl.password` are the API credentials that you get (in the Confluent UI) from your cluster -> Data Integration -> API Keys. `schema_registry_config.basic.auth.user.info` has API credentials for Confluent schema registry which you get (in Confluent UI) from Schema Registry -> API credentials.
 
 When creating API Key for the cluster ensure that the ACLs associated with the key are set like below. This is required for DataHub to read topic metadata from topics in Confluent Cloud.
+
 ```
 Topic Name = *
 Permission = ALLOW
@@ -75,11 +76,11 @@ The Kafka Source uses the schema registry to figure out the schema associated wi
 By default it uses the [Confluent's Kafka Schema registry](https://docs.confluent.io/platform/current/schema-registry/index.html)
 and supports the `AVRO` and `PROTOBUF` schema types.
 
-
 If you're using a custom schema registry, or you are using schema type other than `AVRO` or `PROTOBUF`, then you can provide your own
 custom implementation of the `KafkaSchemaRegistryBase` class, and implement the `get_schema_metadata(topic, platform_urn)` method that
 given a topic name would return object of `SchemaMetadata` containing schema for that topic. Please refer
 `datahub.ingestion.source.confluent_schema_registry::ConfluentSchemaRegistry` for sample implementation of this class.
+
 ```python
 class KafkaSchemaRegistryBase(ABC):
     @abstractmethod
@@ -90,6 +91,7 @@ class KafkaSchemaRegistryBase(ABC):
 ```
 
 The custom schema registry class can be configured using the `schema_registry_class` config param of the `kafka` source as shown below.
+
 ```YAML
 source:
   type: "kafka"
@@ -108,8 +110,8 @@ source:
 
 The current implementation of the support for `PROTOBUF` schema type has the following limitations:
 
-+ Recursive types are not supported.
-+ If the schemas of different topics define a type in the same package, the source would raise an exception.
+- Recursive types are not supported.
+- If the schemas of different topics define a type in the same package, the source would raise an exception.
 
 In addition to this, maps are represented as arrays of messages. The following message,
 
@@ -134,7 +136,7 @@ message MessageWithMap {
 ### Enriching DataHub metadata with automated meta mapping
 
 :::note
-Meta mapping is currently only available for Avro schemas
+Meta mapping is currently only available for Avro schemas, and requires that those Avro schemas are pushed to the schema registry.
 :::
 
 Avro schemas are permitted to have additional attributes not defined by the specification as arbitrary metadata. A common pattern is to utilize this for business metadata. The Kafka source has the ability to transform this directly into DataHub Owners, Tags and Terms.
@@ -150,11 +152,13 @@ Example Avro schema:
   "name": "sampleRecord",
   "type": "record",
   "tags": ["tag1", "tag2"],
-  "fields": [{
-    "name": "field_1",
-    "type": "string",
-    "tags": ["tag3", "tag4"]
-  }]
+  "fields": [
+    {
+      "name": "field_1",
+      "type": "string",
+      "tags": ["tag3", "tag4"]
+    }
+  ]
 }
 ```
 
@@ -178,13 +182,15 @@ Example Avro schema:
   "type": "record",
   "owning_team": "@Data-Science",
   "data_tier": "Bronze",
-  "fields": [{
-    "name": "field_1",
-    "type": "string",
-    "gdpr": {
-      "pii": true
+  "fields": [
+    {
+      "name": "field_1",
+      "type": "string",
+      "gdpr": {
+        "pii": true
+      }
     }
-  }]
+  ]
 }
 ```
 
@@ -212,4 +218,3 @@ config:
 ```
 
 The underlying implementation is similar to [dbt meta mapping](https://datahubproject.io/docs/generated/ingestion/sources/dbt#dbt-meta-automated-mappings), which has more detailed examples that can be used for reference.
-