Apache Kafka is an open-source stream-processing software platform developed by the Apache Software Foundation, written in Scala and Java. Originally created by LinkedIn and subsequently open-sourced, Kafka is designed for fault tolerance, high throughput, scalability, and distributed processing. It functions as a publish-subscribe messaging system, facilitating real-time data pipelines and streaming applications. At its core, Kafka enables the storage and processing of large streams of data in real-time, making it an essential tool in scenarios where high-volume data ingestion, integration, and real-time analytics are critical.
Kafka’s architecture consists of a cluster of servers where each server functions as a broker. The key components of Kafka include producers, which publish data to topics; consumers, which subscribe to these topics and process the data; topics, which are categories for message organization; and partitions within topics for parallel processing. Kafka ensures data durability and reliability through replication, maintaining copies of data across multiple brokers. This robust architecture allows Kafka to seamlessly handle high-throughput workloads in distributed environments, making it a popular choice for event-driven architectures, log aggregation, and operational metrics collection.
This series of tutorials will help you learn Apache Kafka through concise explanations and practical examples.
