Go, Flink, and Spark represent different stages of real-time processing. Go handles ingestion and validation at millisecond latency, Flink maintains continuous event-time computation with state and recovery, and Spark performs large-scale analytical aggregation or feature building over recent and historical data. The choice depends on latency targets, consistency requirements, and the scope of computation.

Apache Spark architecture explained through real-world mechanics: job stages, partitions, shuffle behavior, memory usage, structured streaming, deployment models, and performance tuning strategies in production.