How do you implement real-time data streaming architectures?

Real-time streaming processes data as it arrives rather than in batches. Architecture: event producers (applications, databases, IoT devices, user interactions) emit events to a streaming platform (Apache Kafka, AWS Kinesis, Google Pub/Sub, Azure Event Hubs), stream processors transform and enrich events in real-time (Apache Flink, Kafka Streams, Spark Structured Streaming), and consumers store processed data in serving layers (databases, search indexes, caches, data warehouses). Apache Kafka implementation: topics organize events by category, partitions enable parallel processing and ordering within a key, consumer groups distribute processing across multiple instances, and retention policies determine how long events are stored (hours to infinite). Stream processing patterns: filtering (select events matching criteria), transformation (enrich, reformat, convert), aggregation (count, sum, average over time windows: tumbling, sliding, session), joining (combine streams with other streams or reference data), and windowing (group events into time-based or count-based windows for aggregation). Exactly-once processing: critical for financial and transactional data. Implement using transactional producers and idempotent consumers (Kafka's exactly-once semantics), or implement idempotency at the consumer level using unique event IDs. Change Data Capture (CDC): stream database changes (inserts, updates, deletes) as events using Debezium. Enables real-time data synchronization, cache invalidation, and event-driven architectures without modifying source applications. Use cases: real-time analytics dashboards, fraud detection, IoT sensor processing, recommendation engine updates, inventory tracking, and real-time personalization. Monitoring: track consumer lag (how far behind processing is from production), throughput per topic/partition, processing latency, and error rates.