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R: All content tagged as R in NoSQL databases and polyglot persistence

Calculating a Graph's Degree Distribution Using R MapReduce over Hadoop

Marko Rodriguez is experimenting with R on Hadoop and one of his exercises is calculating a graph’s degree distribution. I confess I had to use Wikipedia for reminding what’s the definition of a node degree:

  1. The degree of a node in a network (sometimes referred to incorrectly as the connectivity) is the number of connections or edges the node has to other nodes. The degree distribution P(k) of a network is then defined to be the fraction of nodes in the network with degree k.
  2. The degree distribution is very important in studying both real networks, such as the Internet and social networks, and theoretical networks.

As an imagination exercise think of a graph database that’s actively maintaining an internal degree distribution and uses it to suggest or partition the graph. Would that work?

Original title and link: Calculating a Graph’s Degree Distribution Using R MapReduce over Hadoop (NoSQL database©myNoSQL)

via: http://groups.google.com/group/gremlin-users/browse_thread/thread/db50a72f92a26e06


Call to Arms: Renjin, R Implementation on JVM Needs Contributions

Until yesterday I didn’t know there’s an attempt to implement the R language on the JVM. But there’s one: renjin. And it sounds like it needs some helping hands to accomplish its goal of reaching a 1.0 release in 2012.

In case you’d wonder why R on the JVM—same question have been asked so many times related to JRuby, Jython, etc—just think of:

  • it would allow access to the tons of Java libraries
  • it would integrate seamlessly with tools like Hadoop

If you are ready to start contributing head on to the Renjin’s plan of attack for 2012 page and learn where your help would be needed.

Original title and link: Call to Arms: Renjin, R Implementation on JVM Needs Contributions (NoSQL database©myNoSQL)


Revolution R Enterprise 5.0 Released

Revolution Analytics, the commercial provider of the leading statistics language for advanced analytics as showed also by this data analysis tools survey among data scientist has released Revolution R Enterprise 5.0 featuring:

  • Distributed/Parallel Computing: Automatically distribute statistical analyses from a desktop across nodes of a cluster through Windows HPC server and distribute R function calls across nodes.
  • Scalable Data Management: Increase flexibility in data analysis with new data import and cleaning/manipulation tools.
  • Integration with Hadoop: Support MapReduce programming in R and integration with HDFS and HBASE with Cloudera Certified Technology
  • Expanded Scalable Analytics Functionality: Apply new big data statistics algorithms including principal components analysis, factor analysis, contingency table analysis and more.
  • Enhanced R Productivity Environment: Create and build R packages with expanded support features.
  • Enhanced RevoDeployR server: Add multiple compute nodes to support more users, batch execution of large analysis jobs, and LDAP enterprise security support.
  • Upgraded Open Source R: Revolution R 5.0 includes the fully-patched R 2.13.2, which features a new byte-compiler to improve performance of user-written functions and packages.

If you are not familiar with R, check this brief description of what is R and how can it help.

Original title and link: Revolution R Enterprise 5.0 Released (NoSQL database©myNoSQL)


R: the Leading Statistics Language and Key Weapon in Advanced Analytics Today

David Smith (Revolution Analytics):

Of course, this isn’t the first time that R has been embedded into a data warehousing appliance. IBM Netezza’s iClass device integrates with Revolution R, and AsterData, the Teradata Data Warehouse Appliance, and Greenplum all provide connections to R as well. Here at Revolution Analytics, we think that such enterprise-level integrations with R serve to grow the R ecosystem and serve as validation of R as a key platform for advanced analytics. As CEO Norman Nie said to GigaOm this weekend, 

“Oracle’s announcement to embed R demonstrates validation for the leading statistics language and offers further evidence that R is a key weapon in advanced analytics today”

And let’s not leave aside the strategic partnership between Revolution Analytics and Cloudera to include RevoConnectR in the CDH.

Original title and link: R: the Leading Statistics Language and Key Weapon in Advanced Analytics Today (NoSQL database©myNoSQL)

via: http://www.r-bloggers.com/oracles-big-data-appliance-to-include-r/


R and Hadoop: Revolution Analytics and Cloudera Partnership Announced

In the series of big announcements coming out this month, Cloudera and Revolution Analytics, the enterprise provider of R software, have announced their partnership to integrate Cloudera’s Hadoop distribution with Revolution R Enterprise platform thus offering R developers direct access to Hadoop data stores and the possibility to write MapReduce jobs directly in R.

The integration packages, named RevoConnectR for Apache Hadoop, are already available freely on GitHub and they will also get commercial support with Revolution R Enterprise 5.0 Server for Linux.

You can read more about this announcement on:

Original title and link: R and Hadoop: Revolution Analytics and Cloudera Partnership Announced (NoSQL database©myNoSQL)


The Appealing Future of Big Data and Data Analytics

In a RWW article, David Smith writes about the R statistics language:

Over two million analysts worldwide use R, and they come from an extremely diverse pool of industries that ranges from journalism to financial services to life sciences.

If you replace R with data analytics, this could seen as a very appealing future of Big Data and data analytics. Something like a generalized version of data analytics at work.

But before loosing myself in this perspective, I thought I should take a look at the present and see how what is done now is going to lead to that amazing tomorrow:

  1. Tim O’Reilly said a couple of years ago “Data is the Intel inside” and since then we’re seeing lots and lots of companies trying to materialize this slogan.
  2. More new technologies for storage, processing, and analysis are developed and reaching the market then in the 10 previous years.
  3. People are starting to embrace big data overcoming their fear of privacy invasion

All these are good signs that we could consider as a good basis for the future. On the other hand the past and today’s reality tell a different story:

  1. Even if technology costs decreased over time, the investment in creating data startups are still high.
  2. Financial institutions are not investing (too much) into data technology companies.
  3. There are only a few companies that are able to accumulate significant amounts of useful data.
  4. There are even fewer companies that are able to use effectively the huge amounts of data.

What worries me is that even if we will continue to see both a commoditization and impressive improvement of data solutions, by the time all tools will be in place and accessible to everyone, as per the opening paragraph, really valuable data will reside in just a few private well locked silos.

Original title and link: The Appealing Future of Big Data and Data Analytics (NoSQL database©myNoSQL)


R: What Is and How Can It Help?

From Loraine Lawson interview with Jeff Erhardt[1].

What is R?

R is an open source statistical programming language. The easiest way to think about it is the largest commercial competitor in the states is a company called SAS, and while it’s not a perfect analogy, one way to think about R is as an open source version of SAS. It’s not perfectly correct, but for people who have not heard of R, that’s one way to explain it.

Where can R help?

  • analyzing and gaining meaning from collected data
  • developing models and extracting the insight from data
  • implementing these analytics within an enterprise and disseminating the knowledge across the enterprise

Now are you ready to bet what will be the data processing platform of tomorrow?


  1. Jeff Erhardt: COO of Revolution Analytics, the company offering products and services for R  

Original title and link: R: What Is and How Can It Help? (NoSQL databases © myNoSQL)

via: http://www.itbusinessedge.com/cm/community/features/interviews/blog/the-power-of-r-more-companies-using-language-for-business-analytics/?cs=46325


The Data Processing Platform for Tomorrow

In the blue corner we have IBM with Netezza as analytic database, Cognos for BI, and SPSS for predictive analytics. In the green corner we have EMC with Greenplum and the partnership with SAS[1]. And in the open source corner we have Hadoop and R.

Update: there’s also another corner I don’t know how to color where Teradata and its recently acquired Aster Data partner with SAS.

Who is ready to bet on which of these platforms will be processing more data in the next years?


  1. GigaOm has a good article on this subject here  

Original title and link: The Data Processing Platform for Tomorrow (NoSQL databases © myNoSQL)


RStudio: The Free and Open R IDE

For data scientists and not only: RStudio, the R IDE that runs on all major platforms or alongside R on a server and being accessible through a browser. Free and Open.

RStudio

Original title and link: RStudio: The Free and Open R IDE (NoSQL databases © myNoSQL)


R and the web in 2011

The last couple of posts were about BigData and Jeffrey Horner’s presentation is inline with this topic:

If there is ever a time to learn R and web application development, it is now…in the age of Big Data. The upcoming release of R 2.13 will provide basic functionality for developing R web applications on the desktop via the internal HTTP server, but the interface is incompatible with rApache. Jeffrey will talk about Rack, a web server interface and package for R, and how you can start creating your own Big Data stories from the comfort of your own desktop.

Note: The video is missing the beginning and it is not a generic talk about R, so it will be interesting mostly to those using R and planning to develop web applications directly from R.

Original title and link: R and the web in 2011 (NoSQL databases © myNoSQL)


Names You Need to Know in 2011: R Data Analysis Software

Steve McNally (Forbes):

Simply put by one of its staunchest advocates, “R is the most powerful statistical computing language on the planet; there is no statistical equation that cannot be calculated in R.”

If you say data scientists or Big Data, then you are saying Hadoop and R.

Original title and link: Names You Need to Know in 2011: R Data Analysis Software (NoSQL databases © myNoSQL)

via: http://blogs.forbes.com/smcnally/2010/11/10/names-you-need-to-know-in-2011-r-data-analysis-software/


MongoDB and R using Java

It would be nice if there were an R package, along the lines of RMySQL, for MongoDB. For now there is not — so, how best to get data from a MongoDB database into R

I told you before about R and “data addicts”

Original title and link: MongoDB and R using Java (NoSQL databases © myNoSQL)

via: http://nsaunders.wordpress.com/2010/09/24/connecting-to-a-mongodb-database-from-r-using-java/