Continuous analytics: Difference between revisions

Revision as of 19:15, 17 May 2016

Revision as of 19:35, 17 May 2016

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

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Analytics is the application of mathematics and statistics to big data. Data scientists write analytics programs to look for solutions to business problems, like forecasting demand or setting an optimal price.

Traditionally data scientists have not been part of IT development teams, like regular Java programmers. This is because their skills set them apart in their own department not normally related to IT., i.e., math, statistics, and data science. So it is logical to conclude that their approach to writing software code does not enjoy the same efficiencies as the traditional programming team. In particular traditional programming has adopted the Continuous ReleaseDelivery approach to writing code and the Agile methodology. That releases software in a continuous circle, called iterations. Because operating that way has become commonplace, there are many software tools to do that.

Continuous Analytics then is the extension of the Continuous ReleaseDelivery 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, like Apache Spark, 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 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 DefineDefined |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>