2026-06-08 –, Palais Atelier
When does Apache Flink solve real problems versus add complexity? Explore use cases where Flink becomes essential such as fraud detection, CDC, real-time analytics versus when batch or Kafka Streams suffice. Compare stream engines (Flink, Spark) with platforms (Kafka, Pulsar) to confidently decide when streaming delivers value.
Apache Flink promises powerful stream processing, but when does that power translate to actual business value? This session provides the architectural clarity engineers need by focusing on specific use cases where Flink becomes essential versus scenarios where simpler alternatives suffice. Attendees will explore real-world problems that demand Flink's stateful processing and exactly-once guarantees—fraud detection, real-time recommendations, CDC-driven data lakes—contrasted with situations where batch jobs or Kafka Streams are better fits. The talk draws practical distinctions between stream processing engines (Flink versus Spark) and streaming platforms (Kafka with ksqlDB/Kstreams, Pulsar), clarifying when each architectural pattern shines. Engineers will leave equipped to confidently decide when streaming architecture delivers results and when it's unnecessary complexity.
Naci Simsek is a Senior Customer Success Technical Manager at Ververica with over 17 years of experience in IT and Telecom. He began his career as a Customer Support Engineer at Nortel Networks, advancing through roles as Software Engineer, Engineering Team Lead, Project Manager, and Solutions Architect at Huawei. Over nearly a decade, he specialized in customer-facing big data solutions as a Platform Engineer, BI Engineer, and Data Engineer. In his current position, he supports customers in leveraging Apache Flink for real-time data streaming across on-premises and cloud environments.
He holds a Bachelor’s degree in Computer Engineering from Ege University, an MBA from Bahcesehir University, and the PMP® certification.