Spark, Flink, and Kafka Streams Compared
The choice is rarely about features. It is about the latency you actually need, how much state you will carry, and whether you want a cluster to operate.
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Stream Processing Semantics and Correctness
A streaming result describes data received by an agreed boundary. See how windows, lateness, state, and publication rules make revisions understandable.
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Kafka Log, Replication, and Consumption
Order lives inside a partition, durability comes from in-sync replicas, and consumers hold their own place in the log. The rest is still your problem.
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Spark Performance and Cost Factors
More executors help while tasks await slots. Skew, layout, memory, and serialization can set limits that extra machines do not remove.
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Spark Execution Plans, Stages, and Shuffles
Row computation waits for an action, while planning may happen earlier. See how plans become stages and tasks, and where shuffle boundaries cost time.
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When Distributed Processing Is Needed
A cluster is not a size upgrade; it is a different set of costs. What actually forces distribution, and what a single node handles that people assume it cannot.