Batch vs. Stream Processing: When to Use Spark, Flink, or Kafka Streams
Choose between batch and stream processing for your data workload. Compare Apache Spark, Flink, and Kafka Streams on latency, cost, and complexity.
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Tags: data-engineering, streaming, batch
Batch vs. Stream Processing: When to Use Spark, Flink, or Kafka Streams The choice between batch and stream processing is one of the most consequential architectural decisions in a data system. Get it right and your pipeline is simple, cost-effective, and maintainable. Get it wrong and you're either paying for streaming infrastructure you don't need, or your "real-time" product is running on hourly batch jobs that everyone pretends are real-time. This guide cuts through the hype and gives you a practical decision framework. When Batch Is Enough (Most of the Time) Before reaching for streaming, check whether your latency requirement is actually as strict as you think. Batch Is Appropriate When The business decision is made daily — If your marketing team looks at campaign performance…
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