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Pub Sub Hub The Kafka and RabbitMQ Story

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Manage episode 436780450 series 3596044
Content provided by Omid Zamani. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Omid Zamani or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode, we eavesdrop on a fascinating dialogue between Kafka and RabbitMQ, two titans of the messaging world. They discuss their unique approaches to data storage, scalability, latency, and message delivery. The conversation delves into Kafka's collaboration with Apache Flink for real-time sentiment analysis, and RabbitMQ's use of quorum queues. From near real-time to microsecond latencies, from log-based systems to complex pipe structures, this chat illuminates the strengths and trade-offs of each system, offering valuable insights for developers and architects alike.

  continue reading

17 episodes

Artwork
iconShare
 
Manage episode 436780450 series 3596044
Content provided by Omid Zamani. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Omid Zamani or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode, we eavesdrop on a fascinating dialogue between Kafka and RabbitMQ, two titans of the messaging world. They discuss their unique approaches to data storage, scalability, latency, and message delivery. The conversation delves into Kafka's collaboration with Apache Flink for real-time sentiment analysis, and RabbitMQ's use of quorum queues. From near real-time to microsecond latencies, from log-based systems to complex pipe structures, this chat illuminates the strengths and trade-offs of each system, offering valuable insights for developers and architects alike.

  continue reading

17 episodes

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