fix

docs/website/docs/dlt-ecosystem/verified-sources/kafka.md

@@ -104,7 +104,7 @@ For more information, read the [Walkthrough: Run a pipeline](../../walkthroughs/
 `dlt` works on the principle of [sources](../../general-usage/source) and
 [resources](../../general-usage/resource).
 
-### Source `kafka_source`
+### Source `kafka_consumer`
 
 This function retrieves messages from the given Kafka topics.
 
@@ -127,8 +127,9 @@ the `secrets.toml`. May be used explicitly to pass an initialized
 Kafka Consumer object.
 
 `msg_processor`: A function, which'll be used to process every message
-read from the given topics. Can be used explicitly to pass a custom
-processor. See the [default processor](https://github.com/dlt-hub/verified-sources/blob/fe8ed7abd965d9a0ca76d100551e7b64a0b95744/sources/kafka/helpers.py#L14-L50)
+read from the given topics before saving them in the destination.
+Can be used explicitly to pass a custom processor. See the
+[default processor](https://github.com/dlt-hub/verified-sources/blob/fe8ed7abd965d9a0ca76d100551e7b64a0b95744/sources/kafka/helpers.py#L14-L50)
 as an example of how to implement processors.
 
 `batch_size`: The amount of messages to extract from the cluster
@@ -164,7 +165,7 @@ this offset.
    topics = ["topic1", "topic2", "topic3"]
 
    source = kafka_consumer(topics)
-   load_info = pipeline.run(source, write_disposition="replace")
+   pipeline.run(source, write_disposition="replace")
    ```
 
 1. To extract messages and process them in a custom way:
@@ -181,12 +182,12 @@ this offset.
         }
 
     data = kafka_consumer("topic", msg_processor=custom_msg_processor)
-    load_info = pipeline.run(data)
+    pipeline.run(data)
    ```
 
 1. To extract messages, starting from a timestamp:
 
    ```python
     data = kafka_consumer("topic", start_from=pendulum.datetime(2023, 12, 15))
-    load_info = pipeline.run(data)
+    pipeline.run(data)
    ```