Real Time Data Processing and Analytics Questions

Designing and operating real time data ingestion and analytics pipelines that support low latency processing and high throughput. Topics include event streaming architectures, publish subscribe brokers, stream processing engines and stateful operators; ordering guarantees and delivery semantics and strategies for handling duplicates and out of order events; windowing, time semantics, and handling late arriving data; partitioning and scaling strategies, state management and checkpointing; backpressure and flow control; retention and compaction strategies for event logs; connectors to operational databases and analytics stores and use of change data capture to feed streams; schema evolution, data lineage, and downstream aggregation for dashboards and machine learning; and monitoring, alerting, and runbooks for streaming infrastructure.