To develop a Python program that communicates with a Kafka cluster, you'll need to install the `kafka-python` library. You can do this using pip:

```bash
pip install kafka-python
```

Now let's create a simple producer and consumer for a Kafka topic.

1. Producer: This will produce messages to a Kafka topic.
2. Consumer: This will consume messages from the same Kafka topic.
3. Processing: We'll use a simple word count algorithm to process the consumed messages.

Here's the complete code:

```python
from kafka import KafkaProducer, KafkaConsumer
import json
from collections import defaultdict

# Function to create a Kafka producer
def create_producer():
    return KafkaProducer(bootstrap_servers='localhost:9092',
                         value_serializer=lambda v: json.dumps(v).encode('utf-8'))

# Function to create a Kafka consumer
def create_consumer(topic):
    return KafkaConsumer(topic,
                         bootstrap_servers='localhost:9092',
                         value_deserializer=lambda m: json.loads(m.decode('utf-8')))

# Function to send messages to a Kafka topic
def send_messages(producer, topic, messages):
    for message in messages:
        producer.send(topic, message)
    producer.flush()

# Function to consume messages from a Kafka topic and process them using a word count algorithm
def consume_and_process_messages(consumer):
    word_count = defaultdict(int)
    
    for message in consumer:
        words = message.value.split()
        
        for word in words:
            word_count[word] += 1
        
        print("Updated Word Count:", dict(word_count))

if __name__ == "__main__":
    # Create a Kafka producer
    producer = create_producer()

    # Send messages to the Kafka topic
    topic = 'test-topic'
    messages = ["Hello world", "Kafka is great", "Python is awesome"]
    send_messages(producer, topic, messages)

    # Create a Kafka consumer
    consumer = create_consumer(topic)

    # Consume and process messages from the Kafka topic
    consume_and_process_messages(consumer)
```

Replace `'localhost:9092'` with your Kafka cluster's bootstrap server address. This example assumes you have a running Kafka instance on your local machine.

To run this program, make sure you have a Kafka broker running locally (or update the `bootstrap_servers` parameter to point to your remote Kafka broker). Then simply execute the Python script:

```bash
python kafka_producer_consumer.py
```

The output should show the updated word count for each message consumed from the Kafka topic:

```
Updated Word Count: {'Hello': 1, 'world': 1}
Updated Word Count: {'Hello': 1, 'world': 1, 'Kafka': 1, 'is': 1, 'great': 1}
Updated Word Count: {'Hello': 1, 'world': 1, 'Kafka': 1, 'is': 1, 'great': 1, 'Python': 1, 'awesome': 1}
```

This is a simple example of producing and consuming messages using Kafka in Python. You can modify the processing function to implement more complex algorithms like sentiment analysis or other data processing tasks.
Develop a Python program that communicates with a Kafka cluster, produces messages to a topic, consumes messages from the same topic, and processes the consumed messages using a specified algorithm (e.g., word count, sentiment analysis).