When engineering real-time data pipelines, selecting the wrong compute engine can result in excessive latency, bloated cloud compute bills, and state management nightmares.
The primary architectural debate centers around true event-driven stream processing (Apache Flink) versus micro-batch execution (Spark Structured Streaming).
Micro-Batching vs. True Stream Processing
Apache Spark processes events by collecting them into small discrete batches (typically 100ms to several seconds). While highly efficient for high-throughput analytical workloads, micro-batching introduces inherent latency that is unacceptable for fraud prevention or algorithmic arbitrage.
Apache Flink, by contrast, operates on an event-by-event model with stateful in-memory processing. Each record triggers computation the millisecond it arrives from the Kafka partition, delivering true sub-20ms latency guarantees.
When to Choose Each Engine
Use Apache Flink when: you require sub-100ms latency, complex temporal session windows, or stateful CEP (Complex Event Processing).
Use Apache Spark when: latency requirements exceed 1-2 seconds, your pipelines combine large historical datasets with streaming inputs, or your team already possesses deep PySpark expertise.