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Apache Kafka - distributed streaming platform event streaming

Apache Kafka - Distributed Streaming Platform

What is Apache Kafka?

Apache Kafka is a distributed streaming system created by LinkedIn in 2011. Designed to handle real-time event streams, it offers high throughput, fault tolerance, and horizontal scalability.

First released
2011
Creator
LinkedIn
Type
Distributed Streaming Platform
License
Apache 2.0
1T+
Messages daily
50k+
Messages/second
80%
Fortune 100 uses
See Kafka advantagesKafka SoftwareLogic projects
kafka-producer.java
// Apache Kafka Producer Example
import org.apache.kafka.clients.producer.*;
import java.util.Properties;
public class KafkaProducerExample {
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
Producer<String, String> producer =
new KafkaProducer<>(props);
for (int i = 0; i < 1000; i++) {
ProducerRecord<String, String> record =
new ProducerRecord<>("user-events", "User action " + i);
producer.send(record);
}
producer.close();
}
}
Output:
Kafka producer started...
✓ Connected to localhost:9092
Sending 1000 messages to topic 'user-events'
Throughput: 50,000 messages/second
All messages sent successfully!
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Advantages Apache Kafka - why choose event streaming

Key Kafka benefits: high throughput, fault tolerance, scalability, real-time processing, microservices communication

Disadvantages Apache Kafka - challenges and limitations

Operational complexity, infrastructure overhead, learning curve and other challenges of Kafka implementation in enterprise

Use Cases Apache Kafka - business applications

Practical Kafka applications: event streaming, microservices, log aggregation, real-time analytics in modern architecture

Event streaming architectures

Event-driven architecture, CQRS, Event Sourcing, real-time data pipelines between microservices

tech.useCases.frameworks
Kafka StreamsApache FlinkSpark Streaming
tech.useCases.realExamples

Netflix content recommendations, Uber ride matching, LinkedIn activity feeds

Microservices communication

Asynchronous communication, publish-subscribe patterns, saga patterns, distributed transactions

tech.useCases.frameworks
Spring BootNode.js.NET Core
tech.useCases.realExamples

E-commerce order processing, payment workflows, inventory management systems

Log aggregation

Centralized logging, metrics collection, distributed tracing, application monitoring

tech.useCases.frameworks
ELK StackFluentdLogstash
tech.useCases.realExamples

Application logs, server metrics, user activity tracking, system health monitoring

Real-time analytics

Stream processing, real-time analytics, machine learning pipelines, IoT data ingestion

tech.useCases.frameworks
Apache DruidClickHouseApache Pinot
tech.useCases.realExamples

Fraud detection, personalization engines, IoT sensor data, financial trading systems

FAQ: Apache Kafka – Frequently Asked Questions

Complete answers about Kafka - from event streaming to choosing between Kafka vs RabbitMQ, performance and business benefits

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