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In this session, we will explore the stream processing capabilities for Kafka and compare the three popular options: Kafka Streams, ksqlDB, and Apache Flink®. We will dive into the strengths and limitations of each technology, and compare them based on their ease of use, performance, scalability, and flexibility. By the end of the session, attendees will have a better understanding of the different options available for stream processing with Kafka, and which technology might be the best fit for their specific use case. This session is ideal for developers, data engineers, and architects who want to leverage the power of Kafka for real-time data processing.

Bio:

Before Jan Svoboda started his Apache Kafka journey at Confluent, he worked as an Advisory Platform Architect at Pivotal and DevOps Solutions Architect at IBM, among others. Jan joined Confluent in April 2020 as a Solutions Engineer, establishing microservices development as his favourite topic. Jan holds degrees in Management of Information Systems from UNYP and Computer Science from UCF.

Salomon will showcase our open-source approach helping data engineers to define complex Kafka pipelines consisting of multiple interconnected Kafka Streams applications, Kafka Producers/Consumers, and Kafka Connectors in a straightforward manner. Our ready-to-use approach provides automated lifecycle management of Kafka pipelines on Kubernetes, i.e., handling deployment, update, destroy, reset, and clean-up transparently. In addition, we demonstrate how this approach provides a comprehensible view of all Kafka pipelines and their metrics.