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To make this work means getting [[data scientists]] to write their code in the same [[code repository]] that regular programmers use so that software can pull it from there and run it through the build process. It also means saving the configuration of the big data cluster (sets of [[Virtual machine|virtual machines]]) in some kind of repository as well. That facilitates sending out analytics code and big data software and objects in the same automated way as the continuous integration process.<ref>{{cite web|url=http://southernpacificreview.com/2016/05/17/continuous-analytics-defined/|title=Continuous Analytics Defined |website=Southern Pacific Review|publisher=Southern Pacific Review|accessdate=17 May 2016}}</ref><ref>{{cite web|last1=Pushkarev|first1=Stepan|title=Tear down the Wall between Data Science and DevOps|url=https://www.linkedin.com/pulse/tear-down-wall-between-data-science-devops-stepan-pushkarev?trk=prof-post|website=LinkedIN|publisher=LinkedIN|accessdate=17 May 2016}}</ref>
<ref>{{cite web|title=Data Wow|url=https://datawow.io|website=datawow.io|accessdate=12 January 2021}}</ref><ref>[https://datasciencericardo.com Data Scientist Ricardo Ramon Benitez]</ref>
== External links ==
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