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.

How to stream data from sharded PostgreSQL to a Data Warehouse using Debezium and Kafka. This guide covers Change Data Capture (CDC) setup with Kubernetes, handling sharded databases, and overcoming operational challenges for scalable, real-time analytics.