Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications. It is designed to handle high-throughput, fault-tolerant, and scalable messaging.
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Publish and Subscribe: Kafka allows applications to publish and subscribe to streams of records, similar to a message queue or enterprise messaging system.
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Durability and Reliability: Kafka stores streams of records in a fault-tolerant way, ensuring data durability and reliability. It replicates data across multiple nodes to prevent data loss.
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Scalability: Kafka is designed to scale horizontally by adding more brokers (servers) to the cluster, which can handle increased load and data volume.
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High Throughput: Kafka is optimized for high throughput, making it suitable for processing large volumes of data in real-time.
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Topics and Partitions: Data in Kafka is organized into topics, which are further divided into partitions. Each partition is an ordered, immutable sequence of records that is continually appended to.
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Producers and Consumers: Producers are applications that publish data to Kafka topics, while consumers are applications that subscribe to topics and process the data.
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Use Cases: Kafka is commonly used for log aggregation, real-time analytics, data integration, stream processing, and building event-driven architectures.
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Integration: Kafka integrates well with various data processing frameworks like Apache Spark, Apache Flink, and Apache Storm, as well as with databases and other data storage systems.