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 for financial services: stateful aggregations, windowing semantics, temporal joins for sanctions screening, schema management, and the operational trade-offs that determine which engine fits your architecture.
End-to-end production design: Feast feature store, sliding-window velocity checks with Flink KeyedProcessFunction, embedded ONNX model scoring, a dispute-driven feedback loop, and the operational patterns that keep sub-100 ms card authorisation SLAs intact under load.