Batch vs Stream Processing: Spark, Flink, or Kafka
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. --- The Core Difference Batch processing — Process a bounded dataset (a file, a day's worth of records, a table) from beginning to end, then stop. The data is at rest when you start. Stream processing — Process an unbounded, continuously arriving dataset. Records are…
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