What is Apache Kafka? A Guide to the Distributed Streaming Platform

In today’s world, billions of data sources generate streams of events every second. These events, ranging from a customer placing an online order to a sensor reporting temperature changes, drive processes and decisions in real-time. This ever-growing need for handling and processing massive amounts of streaming data has led to the rise of platforms like Apache Kafka, a distributed streaming platform that has transformed the way organizations handle real-time data.

What is Apache Kafka?

Apache Kafka is an open-source, distributed streaming platform designed to handle real-time data streams reliably and at scale. Originally developed by LinkedIn in 2011 and later open-sourced to the Apache Software Foundation, Kafka has become a cornerstone for building event-driven applications, streaming pipelines, and data processing systems.

Kafka’s core capabilities include:

1. Publishing and subscribing to event streams in real time.
2. Storing data streams in a fault-tolerant, durable, and ordered manner.
3. Processing data streams as they arrive.

Key Capabilities of Apache Kafka

1. High Throughput and Scalability

Thanks to its distributed architecture, Kafka can ingest and process millions of events per second. Data is partitioned and replicated across multiple brokers, enabling the platform to scale horizontally while maintaining high performance.

2. Durable and Fault-Tolerant

Kafka ensures durability by writing all messages to disk, with replication across brokers. Even in the case of hardware or software failures, Kafka guarantees data reliability and integrity.

3. Real-Time Processing

Kafka processes streams as they occur, allowing applications to react to events immediately. This is critical for building real-time analytics, monitoring systems, and responsive user experiences.

4. Rich APIs

Kafka provides four powerful APIs to enable diverse use cases:

• Producer API: Publishes streams of records to Kafka topics.
• Consumer API: Subscribes to one or more topics to read and process records.
• Streams API: Enables complex stream processing, such as filtering, aggregating, and transforming data.
• Connector API: Simplifies integration with external systems through pre-built or custom connectors.

Use Cases of Apache Kafka

1. Real-Time Streaming Data Pipelines

Kafka is ideal for moving large volumes of data between systems in real time. For example:

• Streaming logs from applications to monitoring platforms.
• Moving financial transactions from customer-facing apps to fraud detection systems.

2. Event-Driven Applications

Kafka powers event-driven architectures where each event triggers subsequent actions. Examples include:

• E-commerce applications updating inventory and sending order confirmation emails.
• IoT devices sending sensor readings for immediate analysis.

3. Real-Time Analytics

Businesses use Kafka to analyze clickstreams, customer behavior, and operational metrics in real-time, enabling better decision-making.

Kafka vs. RabbitMQ: Key Differences

While both Kafka and RabbitMQ are messaging solutions, their purposes differ significantly:

Kafka’s Integration with Other Technologies

Kafka is often used with other Apache tools to build robust data architectures:

1. Apache Spark

Spark processes data streams from Kafka for real-time analytics or machine learning applications.

2. Apache NiFi

NiFi’s drag-and-drop interface makes it easy to integrate Kafka as a producer or consumer, simplifying data flow management.

3. Apache Flink

Flink adds low-latency computation to Kafka streams, enabling advanced analytics and event processing.

4. Apache Hadoop

Kafka acts as a pipeline for real-time data ingestion into Hadoop’s distributed storage for further analysis and processing.

Steps to Get Started with Apache Kafka

Step 1: Get Kafka

Download the latest Kafka release from the official Kafka website.

Extract the downloaded archive:

$ tar -xzf kafka_2.13-3.9.0.tgz
$ cd kafka_2.13-3.9.0

Step 2: Start the Kafka Environment

Prerequisites: Ensure Java 8+ is installed on your system.

Option 1: Kafka with KRaft

• Generate a Cluster UUID:

$ KAFKA_CLUSTER_ID="$(bin/kafka-storage.sh random-uuid)"

• Format Log Directories:

$ bin/kafka-storage.sh format --standalone -t $KAFKA_CLUSTER_ID -c config/kraft/reconfig-server.properties

• Start the Kafka Server:

$ bin/kafka-server-start.sh config/kraft/reconfig-server.properties

Alternatively, use Docker images for KRaft:

JVM-based Kafka Image:

$ docker pull apache/kafka:3.9.0
$ docker run -p 9092:9092 apache/kafka:3.9.0

• GraalVM-based Kafka Image:

$ docker pull apache/kafka-native:3.9.0
$ docker run -p 9092:9092 apache/kafka-native:3.9.0

Option 2: Kafka with ZooKeeper

• Start ZooKeeper:

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

• Start Kafka Broker (in another terminal):

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

Step 3: Create a Topic

• Create a topic to store events:

$ bin/kafka-topics.sh --create --topic quickstart-events --bootstrap-server localhost:9092

• Verify the topic details:

$ bin/kafka-topics.sh --describe --topic quickstart-events --bootstrap-server localhost:9092

Step 4: Write Events into the Topic

• Run the producer client:

$ bin/kafka-console-producer.sh --topic quickstart-events --bootstrap-server localhost:9092

• Enter events:

> This is my first event
> This is my second event

Step 5: Read Events from the Topic

• Run the consumer client (in a new terminal):

$ bin/kafka-console-consumer.sh --topic quickstart-events --from-beginning --bootstrap-server localhost:9092

You should see the events you added.

Step 6: Import/Export Data with Kafka Connect

Edit the plugin.path property in config/connect-standalone.properties to include the path of connect-file-3.9.0.jar:

$ echo "plugin.path=libs/connect-file-3.9.0.jar" >> config/connect-standalone.properties

• Create test data:

$ echo -e "foo\nbar" > test.txt

• Start connectors:

$ bin/connect-standalone.sh config/connect-standalone.properties config/connect-file-source.properties config/connect-file-sink.properties

• Verify the output:

$ more test.sink.txt

Step 7: Process Events with Kafka Streams

Use Kafka Streams to implement real-time applications. Example for WordCount in Java:

KStream<String, String> textLines = builder.stream("quickstart-events");
KTable<String, Long> wordCounts = textLines
    .flatMapValues(line -> Arrays.asList(line.toLowerCase().split(" ")))
    .groupBy((keyIgnored, word) -> word)
    .count();
wordCounts.toStream().to("output-topic", Produced.with(Serdes.String(), Serdes.Long()));

Step 8: Terminate the Kafka Environment

• Stop the producer and consumer clients using Ctrl+C.
• Stop the Kafka broker and ZooKeeper (if used) using Ctrl+C.
• Clean up logs:

$ rm -rf /tmp/kafka-logs /tmp/zookeeper /tmp/kraft-combined-logs

Conclusion

Apache Kafka is more than just a messaging platform—it’s the backbone of modern data-driven applications. Its distributed architecture, scalability, and real-time capabilities make it indispensable for enterprises looking to build robust, responsive, and efficient systems. Whether you’re designing a streaming data pipeline, an event-driven application, or a real-time analytics engine, Kafka provides the foundation to turn raw data into actionable insights.

As organizations continue to rely on real-time data, the role of Kafka will only grow in importance. Now is the time to explore how Kafka can transform your data architecture and drive innovation in your business.