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Distributed event streaming for real-time data pipelines

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Kafka moves data at massive scale. When millions of events per second need to flow between systems reliably, Kafka provides the highway. Producers publish messages, consumers read them, and nothing gets lost even when systems crash. Topics organize streams of data with configurable retention. Consumer groups enable parallel processing. The distributed architecture means no single point of failure and virtually unlimited throughput. Kafka has become the backbone of modern data architectures. Real-time analytics, event-driven microservices, change data capture—any use case involving high-volume streaming likely runs on Kafka.

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