First, ensure RabbitMQ is installed and running on your system. You can download RabbitMQ from the official website. Follow the installation instructions specific to your operating system. Once installed, start the RabbitMQ server using the appropriate command (e.g., `rabbitmq-server` for Unix systems or using the RabbitMQ service in Windows).

Access the RabbitMQ Management Dashboard, usually available at `http://localhost:15672`, and log in with the default credentials (guest/guest). Navigate to the "Queues" tab and create a new queue where the CSV data will be sent. Give it a descriptive name, e.g., `csv_data_queue`, and configure any necessary parameters like durability or auto-delete.

Choose a programming language to handle the CSV file (Python is a common choice). Use a library like Python's built-in `csv` module to read the file. Write a script to open the CSV file and iterate through each row, extracting the necessary data. Here's a simple Python example:

```python
import csv

def read_csv(file_path):
with open(file_path, mode='r') as file:
csv_reader = csv.DictReader(file)
for row in csv_reader:
yield row
```

Using a RabbitMQ client library in your chosen language, establish a connection to the RabbitMQ server. For Python, you can use the `pika` library. Here's an example of establishing a connection:

```python
import pika

def connect_to_rabbitmq():
connection = pika.BlockingConnection(
pika.ConnectionParameters(host='localhost')
)
channel = connection.channel()
return connection, channel
```

With the data read from the CSV and a connection established, loop through the CSV data and publish each row to the RabbitMQ queue. Ensure the data is serialized to a string or JSON format before sending. Continuing the Python example:

```python
import json

def publish_to_queue(channel, queue_name, data):
for row in data:
message = json.dumps(row)
channel.basic_publish(
exchange='',
routing_key=queue_name,
body=message
)
```

Implement error handling to manage any potential issues while reading the CSV or publishing messages. Ensure that your program can handle connection errors gracefully and retry as necessary. Also, consider using RabbitMQ acknowledgments to confirm that messages are successfully received and processed by consumers.

```python
try:
connection, channel = connect_to_rabbitmq()
data = read_csv("path/to/your/file.csv")
publish_to_queue(channel, 'csv_data_queue', data)
except Exception as e:
print(f"An error occurred: {e}")
finally:
if connection:
connection.close()
```

Use the RabbitMQ Management Dashboard to verify that messages are being delivered to the queue. Monitor the queue to ensure data is being consumed as expected, and no messages are stuck. Adjust your script as needed based on the behavior observed during initial runs.

By following these steps, you can effectively move data from a CSV file to RabbitMQ without relying on third-party connectors or integrations. Adjust the script and configuration to suit your specific requirements and environment.