Project Metamorphosis: Wir präsentieren die Event-Streaming-Plattform der nächsten Generation. Mehr erfahren

Tradeoffs in Distributed Systems Design: Is Kafka The Best?

When choosing an event streaming platform, Kafka shouldn’t be the only technology you look at. There are a plethora of others in the messaging space today, including open source and proprietary software as well as a range of cloud services. So how do you know you are choosing the right one? A great way to deepen our understanding of event streaming and Kafka is exploring the trade-offs in distributed system design and learning about the choices made by the Kafka project. We’ll look at how Kafka stacks up against other technologies in the space, including traditional messaging systems like Apache ActiveMQ and RabbitMQ as well as more contemporary ones, such as BookKeeper derivatives like Apache Pulsar or Pravega. This talk focuses on the technical details such as difference in messaging models, how data is stored locally as well as across machines in a cluster, when (not) to add tiers to your system, and more. By the end of the talk, you should have a good high-level understanding of how these systems compare and which you should choose for different types of use cases.


Ben Stopford

Ben arbeitet als Technologe im Büro des CTO von Confluent und hat bereits an zahlreichen Projekten mitgewirkt, von der Implementierung der aktuellen Version des Replikationsprotokolls von Kafka bis hin zu Entwicklungsstrategien für Streaming-Anwendungen. Bevor Ben zu Confluent kam, war er bei einem großen Finanzinstitut für die Entwicklung und das Design einer unternehmensweiten Datenplattform verantwortlich und arbeitete an verschiedenen service-orientierten Systemen der ersten Stunde im Finanzsektor und bei Thoughtworks.

Michael Noll

Michael Noll is a product manager at Confluent working on Kafka Streams. Previously, Michael was the technical lead of the Big Data platform of .COM/.NET DNS operator Verisign, where he grew the Hadoop, Kafka, and Storm based infrastructure from zero to PetaByte-sized production clusters spanning multiple data centers – one of the largest Big Data infrastructures operated from Europe at the time. He is an experienced tech speaker and avid tech blogger in the Big Data community ( and serves as a technical reviewer for publishers such as Manning in his spare time. Michael received a Master’s in Computer Science and Business, as well as a PhD in Computer Science.