Why Modern Systems Are Built Around Logs, State, and Time
Streaming data did not simply make existing architectures faster. It changed what modern systems treat as the fundamental unit of computation. In older designs, the dominant unit was usually a request, a row, or a scheduled batch. In streaming systems, the dominant unit became a continuously arriving event that could be stored durably, replayed, joined with state, and processed according to event time instead of only wall-clock execution time. Event streaming platforms are designed to read, write, store, and process events across many machines, while modern stream processors are designed for stateful computation over both bounded and unbounded streams. The broader data-processing shift was captured clearly by the Dataflow model, which framed modern inputs as unbounded and out of order and argued that correctness, latency, and cost had to be treated as explicit design tradeoffs.
Continuous change became a first-class input
Before streaming became mainstream, many systems treated the operational...
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