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Criticism of lambda architecture has focused on its inherent complexity and its limiting influence. The batch and streaming sides each require a different code base that must be maintained and kept in sync so that processed data produces the same result from both paths, while attempting to abstract the code bases into a single framework puts many of the specialized tools in each sides ecosystems out of reach.<ref>{{cite web|last1=Krebs|first1=Jay|title=Questioning the Lambda Architecure|url=http://radar.oreilly.com/2014/07/questioning-the-lambda-architecture.html|website=radar.oreilly.com|publisher=Oreilly|accessdate=15 August 2014|ref=krebs}}</ref>
In practice, each of the three layers can be built from any of a number of suitable components. For the serving layer, some implementations have used Cassandra to store data from the speed layer, and Elephant DB to do the same for the batch layer.<ref name=bijnens-slide>Nathan Bijnens, [http://lambda-architecture.net/architecture/2013-12-11-a-real-time-architecture-using-hadoop-and-storm-devoxx/], 11 December 2013, slide 24</ref>
== References ==
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