Apache Kafka is a distributed streaming platform that is used to build real-time data pipelines and streaming applications. It is designed to handle high throughput and low latency, making it an essential tool for massive data processing.

• Topic: A category or feed name to which records are published.
• Partition: A topic is divided into partitions, which are ordered and immutable sequences of records. Each record within a partition has a unique offset.

• Producer: An application that publishes records to one or more Kafka topics.
• Consumer: An application that subscribes to one or more topics and processes the stream of records.

• Broker: A Kafka server that stores data and serves clients.
• Cluster: A group of Kafka brokers working together.

• Zookeeper is used to manage and coordinate Kafka brokers. It helps in leader election for partitions and configuration management.

Kafka's architecture is designed for scalability and fault tolerance. Here is a simplified view:

1. Producers send data to Kafka topics.
2. Kafka Brokers store the data in partitions.
3. Consumers read data from Kafka topics.
4. Zookeeper manages the Kafka brokers and helps in maintaining the cluster state.

1. Download and Install Kafka:

   • Download Kafka from the official website.
   • Extract the downloaded file and navigate to the Kafka directory.

2. Start Zookeeper:

   bin/zookeeper-server-start.sh config/zookeeper.properties
   

3. Start Kafka Broker:

   bin/kafka-server-start.sh config/server.properties
   

Type messages in the console to send them to the test-topic.

You will see the messages produced in the previous step.

1. Install Kafka Python Client:

   pip install kafka-python
   

2. Kafka Producer Code:

   from kafka import KafkaProducer
   
   producer = KafkaProducer(bootstrap_servers='localhost:9092')
   producer.send('test-topic', b'Hello, Kafka!')
   producer.flush()
   

3. Kafka Consumer Code:

   from kafka import KafkaConsumer
   
   consumer = KafkaConsumer('test-topic', bootstrap_servers='localhost:9092', auto_offset_reset='earliest')
   for message in consumer:
       print(f"Received message: {message.value.decode('utf-8')}")
   

• Producer: Connects to the Kafka broker and sends a message to the test-topic.
• Consumer: Connects to the Kafka broker, subscribes to the test-topic, and prints any messages it receives.

• Broker Connection Issues: Ensure that the Kafka broker is running and accessible at the specified bootstrap_servers.
• Topic Configuration: Verify that the topic exists and is correctly configured with the necessary partitions and replication factor.
• Message Encoding: Ensure that messages are correctly encoded and decoded, especially when dealing with non-ASCII characters.

In this section, we covered the basics of Apache Kafka, including its key concepts, architecture, and practical usage. We also walked through setting up a Kafka producer and consumer using Python. Understanding Kafka is crucial for building scalable and real-time data processing systems. In the next module, we will explore other tools and platforms that complement Kafka in the big data ecosystem.