Monthly Archives: September 2013

Building an Infrastructure to Support Data Science Projects (Part 3 of 3) – Installing and Configuring R / RStudio with Pivotal Greenplum Integration

RLogoIn this third and final part (Part 1 of 3, Part 2 of 3) of the series, I walk you through the installation and configuration of R and RStudio.  I also demonstrate how R is integrated with Pivotal Greenplum.  For those of you who don’t know what R is, you can go here for a lot of useful information.  In short, R is a scripting language and runtime environment used for performing complex (or simple) statistical analysis of data. This tool is available for free under the GNU General Public License.  RStudio is a free and open source IDE for R. You can go here for more information about RStudio.

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Building an Infrastructure to Support Data Science Projects (Part 2 of 3) – Installing Greenplum with MADlib

Installing GreenplumIn the first part of this series (Part 1 of 3) we installed and configured CentOS on a virtual machine.  This laid the foundation and made ready an environment that will now be used to install Pivotal Greenplum Community Edition. This edition allows for any use on a single node per Pivotal’s license model.  Also, as part of this tutorial I will be demonstrating how to install MADlib (open-source) libraries into Greenplum.  MADlib provides a rich set of libraries for advanced in-database data analysis and mining which can be called via regular SQL. The installation of Greenplum and MADlib will facilitate some of the data science excercises I will be demonstrating in the near future.

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Building an Infrastructure to Support Data Science Projects (Part 1 of 3) – Creating a Virtualized Environment.

Construction

As with any project or experiment,  infrastructure has to be in place to support the intended work.  For the case of a data science project, the obvious first step is the computing environment.  Simply stated, you can’t do advanced analytics on large data sets without CPU, RAM and Disk. With these items as your foundation, much can be designed, engineered and built.  Before we can walk through a data science project we need to first have hardware and software in place.  For the purposes of the tutorials here on DataTechBlog, a P.C. or laptop with adequate CPU, RAM and disk will suffice.  Further, it is  my plan to use only open or free software and code for all tutorials. You need only a reasonably spec’d computer to accomplish all that we will do here.  This tutorial will walk you through the installation of VMWare Player  and CentOS 6.x (Optimized for Pivotal Greenplum).  This lays the foundation for the next steps which will include the installation of Pivotal Greenplum, MADlib libraries, R, and R-Studio.  When this environment is complete, you will be able to perform many types of “in database” analysis using SQL with MADlib, analysis using R with Greenplum, and analysis with R against flat files or manually entered data.

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