Real-time analytics, fraud detection, and financial telemetry cannot wait for hourly batch jobs. Modern infrastructure demands continuous stateful computation over unbounded event streams with sub-second latencies (Apache Flink, Spark Streaming, Kafka Streams).
Processing continuous data at scale introduces difficult distributed systems challenges: events arrive out of chronological order due to mobile network delays, operator nodes fail mid-calculation, and window aggregations must produce deterministic results regardless of when packets physically reach servers. This module deconstructs stream processing engines: time abstractions, watermark heuristics, stateful window operators, asynchronous barrier checkpointing (Chandy-Lamport), and transactional two-phase commit sinks. You will trace how bounded-out-of-orderness watermarks advance across partitioned task graphs, how RocksDB incremental state checkpoints avoid stop-the-world pauses, and how end-to-end exactly-once guarantees are preserved across failures.