Continuous analytics: Difference between revisions

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Latest revision as of 08:41, 5 January 2025

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Continuous analytics then is the extension of the continuous delivery software development model to the [[big data]] analytics development team. The goal of the continuous analytics practitioner then is to find ways to incorporate writing analytics code and installing big data software into the agile development model of automatically running unit and functional tests and building the environment system with automated tools.

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>