Begin by ensuring that your Kafka environment is up and running. This includes having your Kafka broker and Zookeeper properly configured. Verify that your Kafka topic is set up to receive the data you intend to stream. You can use the Kafka command-line tools to create and manage topics and to check the status of your Kafka cluster.

Access your Databricks account and create a new cluster if you haven't already. Ensure that your cluster has the necessary resources and configurations to handle streaming data. You will also need to have a workspace set up in Databricks where you can write and execute your code.

In your Databricks environment, install the Apache Spark Kafka integration library. This is typically included in Databricks by default, but you should verify its availability. If not present, you can install it via the Databricks library interface using Maven coordinates, for example: `org.apache.spark:spark-sql-kafka-0-10_2.12:`.

Open a new notebook in Databricks. Use the Spark Kafka integration to configure Kafka as a source. You can create a streaming DataFrame by specifying the Kafka server, the topic you want to subscribe to, and any other necessary Kafka configurations such as starting offsets and security protocol if your Kafka setup requires authentication.

```python
kafka_data_df = (spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", ":")
.option("subscribe", "")
.load())
```

Once you have the streaming DataFrame, process it as needed. This might involve parsing the data from its serialized form, applying transformations, or filtering. Use Spark SQL or DataFrame operations to handle this processing. For example, if your data is in JSON format, you can use the `from_json` function to parse it.

```python
from pyspark.sql.functions import from_json, col
from pyspark.sql.types import StructType, StringType

schema = StructType().add("field1", StringType()).add("field2", StringType())
processed_df = kafka_data_df.selectExpr("CAST(value AS STRING)") \
.select(from_json(col("value"), schema).alias("data")) \
.select("data.")
```

Configure the output sink to write the processed data to Databricks Lakehouse. This is typically done by writing the stream to a Delta table. Specify the output mode (e.g., append), the location or Delta table name, and any checkpointing details needed for fault tolerance.

```python
query = (processed_df
.writeStream
.format("delta")
.outputMode("append")
.option("checkpointLocation", "/path/to/checkpoint/dir")
.start("/mnt/delta/your-delta-table"))
```

Once your streaming job is running, monitor it using the Databricks job interface. Check for any errors or performance issues and adjust resource allocation or code optimizations as necessary. Use the streaming metrics and logs available in Databricks to debug and enhance your data pipeline.

By following these steps, you can efficiently move data from Kafka to your Databricks Lakehouse using only the built-in capabilities of Kafka and Databricks.