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This episode of Techsplainers explores Apache Kafka, the open-source event-streaming platform that has become a cornerstone of modern real-time data systems. The episode explains how Kafka helps organizations publish, store, process and consume streams of events with high throughput, low latency and strong reliability.
Listeners are introduced to the concept of event streaming through familiar examples, from customer orders and website clicks to IoT sensor updates, before learning how Kafka differs from traditional message queues by retaining records for replay and independent consumption. The discussion then breaks down Kafka’s core architecture, including topics, partitions, brokers, offsets and replication, along with Kafka’s shift away from ZooKeeper toward KRaft for simpler cluster management.
The episode also covers Kafka’s four primary APIs, its leading use cases in real-time data pipelines, streaming applications, microservices, cloud-native systems and IoT, and its broader ecosystem integrations with tools like Spark, Flink and Cassandra. Finally, it examines how Kafka compares with RabbitMQ and why Kafka is increasingly valuable in AI-driven systems that depend on live, continuously moving data.
Find more information at https://www.ibm.com/think/topics/apache-kafka
Narrated by Ian Smalley
Bahasa Inggeris
Amerika Syarikat
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