The Cost of Correctness in “Real-Time” Systems Like Kafka and Spark
Kafka and Spark are often introduced as the backbone of real-time data platforms, but the phrase usually means something narrower than it sounds. Kafka is built around ordered offsets and durable progress rather than immediate visibility, while Spark Structured Streaming is usually an incremental execution engine that advances work in micro-batches, even when the API looks continuous. Both systems can deliver low latency, but they do so by trading among batching, ordering, replay, watermarking, checkpointing, and sink semantics. The result is frequently fast enough for operational analytics and alerting, yet still very different from a system that guarantees instant observation or fixed-time completion for every event.
Near Real Time Is Still Not Real Time
The easiest way to misunderstand a Kafka-to-Spark pipeline is to collapse freshness, correctness, and durability into a single slogan. A record may wait in a producer buffer, wait for acknowledgments from brokers, wait for a consumer...
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