1. Install Google Cloud SDK: Follow the instructions at https://cloud.google.com/sdk/install to install and initialize the Google Cloud SDK for your operating system.

2. Install Kafka: Download and install Apache Kafka from https://kafka.apache.org/downloads. Follow the Quickstart guide to get a basic Kafka environment running: https://kafka.apache.org/quickstart.

3. Set up a Kafka topic: Create a topic in Kafka to which you'll publish the BigQuery data.

   ```shell

   bin/kafka-topics.sh --create --topic your-topic-name --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1

   ```

1. Export data to Google Cloud Storage: Use the BigQuery interface or the `bq` command-line tool to export your dataset to Google Cloud Storage (GCS) as CSV, JSON, or Avro files.

   ```shell

   bq extract 'your-project:your_dataset.your_table' gs://your-bucket/your-file-name.json

   ```

2. Download the data: Once the data is in GCS, you can download it to your local machine or a machine that can access both GCS and Kafka.

   ```shell

   gsutil cp gs://your-bucket/your-file-name.json ./local-path/

   ```

1. Choose a programming language: Select a programming language that you are comfortable with and that has good support for Kafka clients (Java, Python, etc.).

2. Set up your development environment: Ensure you have the appropriate Kafka client library installed for your chosen language. For Java, you would use the Kafka Java client. For Python, you could use `confluent-kafka-python` or `kafka-python`.

3. Read data from the exported files: Write code to read data from the files you've exported from BigQuery and downloaded to your local machine.

4. Produce messages to Kafka: For each record in the file, produce a message to the Kafka topic you created earlier.

   Here's a very simplified example in Python using `kafka-python`:

   ```python

   from kafka import KafkaProducer

   import json

   # Set up the Kafka producer

   producer = KafkaProducer(bootstrap_servers='localhost:9092',

                            value_serializer=lambda v: json.dumps(v).encode('utf-8'))

   # Read the data from the file

   with open('your-file-name.json', 'r') as file:

       for line in file:

           record = json.loads(line)

           # Send the data to Kafka

           producer.send('your-topic-name', record)

   producer.flush()

   ```

1. Execute your application: Run the application that you wrote in Step 3 to start the data transfer from the local files to Kafka.

2. Verify the data in Kafka: Use Kafka consumer tools to verify that the data is being published to the topic correctly.

   ```shell

   bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic your-topic-name --from-beginning

   ```

1. Automate the process: Once you've verified that the data transfer works, you can automate the process using a scheduler like cron or Airflow.

2. Monitor your Kafka cluster: Ensure that your Kafka cluster is monitored for uptime, performance, and errors.