Real-Time Stream Processing Real-time data processing operates on continuous, unbounded streams of events, delivering results with latency constraints that vary by application. In contrast to batch processing, which aggregates fixed datasets for periodic analysis, streaming systems ingest and transform events as they arrive, maintaining state across an infinite sequence. Latency requirements differ significantly across domains. For algorithmic trading, sub-millisecond delays are critical to capitalize on market fluctuations. In ride-sharing or delivery tracking, latencies up to 1–5 seconds suffice for updating user interfaces with vehicle positions or estimated arrival times. Key challenges include preserving event order despite network variability, ensuring exactly-once processing to avoid duplicates, performing deduplication on redundant events, and managing persistent state for aggregations or joins under failures.…