Lambda Architecture is a structural architectural principle for large-scale data processing that separates batch and speed pipelines. It defines batch, speed and serving layers to combine accuracy with low latency. Typical decisions involve trade-offs around consistency, complexity and operational cost in data integration.
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Lambda architecture is a data-processing pattern that processes the same data through a batch path and a fast real-time path, then combines both results in a serving layer.
Nathan Marz coined Lambda architecture in the context of large-scale data and distributed computing. It addressed the tension between complete but slow batch computation and fast, potentially incomplete real-time processing; streaming frameworks now offer alternative implementations.
Think of two roads leading to one destination: the batch layer computes a complete baseline, the speed layer adds recent events, and the serving layer answers queries from both results.
It processes the complete dataset and corrects earlier approximations.
It processes new events with low delay until batch computation catches up.
The pattern shows how systems can combine completeness and freshness. It is useful when queries must respond immediately while a full recomputation remains feasible on a regular basis.
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