Streaming Analytics with Spark and Flink
Streaming Analytics with Spark and Flink Streaming analytics helps teams react to data as it arrives. Spark and Flink are two popular engines for this work. Spark shines with a unified approach to batch and streaming and a large ecosystem. Flink focuses on continuous streaming with low latency and strong state handling. Both can power dashboards, alerts, and real-time decisions. Differences in approach Spark is versatile for mixed workloads, pairing batch jobs with streaming via Structured Streaming. It’s easy to reuse code from ETL jobs. Flink is built for true stream processing, with fast event handling, fine-grained state, and low latency guarantees. Spark often relies on micro-batching, while Flink aims for record-by-record processing in most cases. Choosing the right tool ...