Real-time data pipelines, stream processing, and high-throughput event handling for financial data flows, market feeds, fraud detection, and the trade-offs between Kafka Streams and Apache Flink.
Stream processing for fraud detection and real-time payments: windowing, stateful joins, exactly-once processing, and the trade-offs between Kafka Streams (in-process) and Flink (separate cluster).
SQL on streams: when to use it, how to manage stateful queries, and how it fits alongside batch warehouses.
An end-to-end design: feature store, sliding-window aggregates, model scoring on the stream, and feedback loops.