Compare batch and real-time stream processing architectures for data pipelines
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A data pipeline diagram shows where data comes from, the jobs that move and transform it, and where it lands. Describe yours and get an editable diagram with the right icons in seconds.
Design Kafka deployments showing topics, producers, consumers, Kafka Streams, and Kafka Connect
Design Apache Airflow DAGs showing task dependencies, operators, sensors, and workflow orchestration
Common questions about batch vs stream processing comparison
Batch for historical analysis, periodic reports, complete data scans, cost-sensitive workloads. Stream for real-time insights, event-driven actions, continuous monitoring. Visualize use case fit.
Batch uses Spark, Hadoop MapReduce processing complete datasets. Streaming uses Kafka Streams, Flink, Spark Streaming for continuous processing. Show how each handles windowing and state.
Combines batch layer (complete, accurate), speed layer (real-time, approximate), and serving layer merging results. Visualize how both layers process same data with different latencies.
Yes! Kappa architecture uses only streaming but can replay for batch-like processing. Show how to unify batch and stream processing with tools like Apache Beam.
Batch is simpler, cheaper (run when needed), easier to debug. Streaming is complex, always-on (more expensive), requires careful state management. Visualize TCO and operational overhead.
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