Real Time Analytics with Spark and Flink
Real Time Analytics with Spark and Flink Real-time analytics helps teams see events as they happen. Spark and Flink are two mature engines that power streaming pipelines. Each has strengths, so many teams use them together or pick one based on the job. The choice often depends on latency, state, and how you want to grow your data flows. Spark shines when you already run batch workloads or want to mix batch and streaming with a unified API. Flink often wins on low latency and long-running stateful tasks. Knowing your latency needs, windowing, and state size helps you choose. Both systems work well with modern data buses like Kafka and with cloud storage for long-term history. ...