Apache Kafka is an open-source distributed event-streaming platform that facilitates real-time data pipelines and applications. Originally developed by LinkedIn and now managed by the Apache Software Foundation, Kafka allows organizations to publish, subscribe to, store, and process data streams in real time. It is built to be highly scalable, fault-tolerant, and ideal for low-latency data pipelines.
Core Concepts
1. Producer: A client application that publishes messages (events) to Kafka topics. Producers can partition data to balance the load across a Kafka cluster.
2. Consumer: A client application that subscribes to Kafka topics to read and process events in real time. Consumers can join consumer groups for parallel message processing.
3. Broker: Kafka servers (brokers) manage message storage and transmission, support data replication, and ensure high availability.
4. Topic: A logical channel where data is stored and accessed. Topics consist of partitions, which enable Kafka’s scalability and data retention.
5. Partition: Each topic is divided into partitions, allowing Kafka to scale horizontally by processing messages in parallel.
6. ZooKeeper (now optional): Previously, Kafka relied on ZooKeeper for cluster coordination, but recent versions allow self-managed Kafka clusters without ZooKeeper.
Key Features
• High Throughput and Low Latency
• Scalability and Fault Tolerance
• Durable Storage and Exactly-Once Processing
Common Use Cases
• Real-Time Analytics
• Event Sourcing
• Data Integration
• Log Aggregation
• Microservices Communication
Challenges
• Operational Complexity
• Latency over Long Distances
• Message Ordering
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