Continuous analytics: Difference between revisions

Revision as of 19:38, 17 May 2016

Revision as of 05:08, 18 May 2016

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Continuous Analytics is a software development process for releasing analytics code in a manner similar to [http://martinfowler.com/bliki/ContinuousDelivery.html Continuous Delivery] or [https://www.wikiwand.com/en/Continuous_integration Continuous Integration] for traditional Java development projects and Agile.

'''Analytics and Continuous Analytics Defined'''

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To make this work means getting data scientists to write their Scala, Python, and R code in the same code repository that regular programmers use, like Git or Subversion, so that software like Jenkins can pull it from there and run it through the build process. It also means saving the configuration of the the big data cluster (sets of virtual machines) in some kind of repository as well, like Docker. 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>

==References==

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