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Lambda architecture
Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods. This approach to architecture attempts to balance latency, throughput, and fault-tolerance by using batch processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data. The two view outputs may be joined before presentation. The rise of lambda architecture is correlated with the growth of big data, real-time analytics, and the drive to mitigate the latencies of map-reduce.〔 Interview with Nathan Marz, 6 April 2014〕
Lambda architecture depends on a data model with an append-only, immutable data source that serves as a system of record.〔Bijnens, Nathan. ("A real-time architecture using Hadoop and Storm" ). 11 December 2013.〕 It is intended for ingesting and processing timestamped events that are appended to existing events rather than overwriting them. State is determined from the natural time-based ordering of the data.
==Overview==
Lambda architecture describes a system consisting of three layers: batch processing, speed (or real-time) processing, and a serving layer for responding to queries.〔Marz, Nathan; Warren, James. ''Big Data: Principles and best practices of scalable realtime data systems''. Manning Publications, 2013.〕 The processing layers ingest from an immutable master copy of the entire data set.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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