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. It will also touch on the key differences between event-driven streaming and analytical real-time use cases, and compare where real-time OLAP engines, real-time streaming databases, and newer streaming storage approaches fit into the overall architecture. Engineers will leave equipped to confidently decide when streaming architecture delivers results and when it’s unnecessary complexity.