SAS Data Loader for Hadoop is now a 90 day free trial



SAS Data Loader for Hadoop eliminates the complexities of writing MapReduce code, with a simple, point-and-click interface that empowers business analysts to prepare, integrate and cleanse big data faster and easier than ever. In addition, data scientists and programmers can run SAS code on Hadoop in parallel for better performance and greater productivity.


Get Started

  1. Download and install Cloudera QuickStart VM for CDH 5.3x.
  2. Download and install either VMware Player 6.0 or later (for Windows) or VMware Fusion for OS X 6.0 (for Mac).
  3. Download and install your 90-day free trial of SAS Data Loader for Hadoop.




Installing Scala on CentOS

Scala files are now here

tar xvf scala-2.10.1.tgz
sudo mv scala-2.10.1 /usr/lib
sudo ln -s /usr/lib/scala-2.10.1 /usr/lib/scala
export PATH=$PATH:/usr/lib/scala/bin
scala -version


Think Big Analytics

I came across this lovely analytics company. Think Big Analytics. and I really liked their lovely explanation of the whole she-bang big data etc stuff. Because Hadoop isnt rocket science and can be made simpler to explain and deploy.

Check them out yourself at

Also they have an awesome series of lectures coming up-

check out

Up and Running with Big Data: 3 Day Deep-Dive

Over three days, explore the Big Data tools, technologies and techniques which allow organisations to gain insight and drive new business opportunities by finding signal in their data. Using Amazon Web Services, you’ll learn how to use the flexible map/reduce programming model to scale your analytics, use Hadoop with Elastic MapReduce, write queries with Hive, develop real world data flows with Pig and understand the operational needs of a production data platform

Day 1:

  • MapReduce concepts
  • Hadoop implementation:  Jobtracker, Namenode, Tasktracker, Datanode, Shuffle & Sort
  • Introduction to Amazon AWS and EMR with console and command-line tools
  • Implementing MapReduce with Java and Streaming

Day 2:

  • Hive Introduction
  • Hive Relational Operators
  • Hive Implementation to MapReduce
  • Hive Partitions
  • Hive UDFs, UDAFs, UDTFs

Day 3:

  • Pig Introduction
  • Pig Relational Operators
  • Pig Implementation to MapReduce
  • Pig UDFs
  • NoSQL discussion

R and Hadoop #rstats

Lovely ppt from the formidable Jeffrey Bean, whose lucid style in explaining R has made me a big fan of his awesome work!

Take at look at his extensive collection of Big Data with R slides  at – they are both very comprehensive and a delightful addition to anyone wishing to go the cloud, hadoop, R  route
His blog at talks of lots of very relevant topics.

Interview James G Kobielus IBM Big Data

Here is an interview with  James G Kobielus, who is the Senior Program Director, Product Marketing, Big Data Analytics Solutions at IBM. Special thanks to Payal Patel Cudia of IBM’s communication team,for helping with the logistics for this.

Ajay -What are the specific parts of the IBM Platform that deal with the three layers of Big Data -variety, velocity and volume

James-Well first of all, let’s talk about the IBM Information Management portfolio. Our big data platform addresses the three layers of big data to varying degrees either together in a product , or two out of the three or even one of the three aspects. We don’t have separate products for the variety, velocity and volume separately.

Let us define these three layers-Volume refers to the hundreds of terabytes and petabytes of stored data inside organizations today. Velocity refers to the whole continuum from batch to real time continuous and streaming data.

Variety refers to multi-structure data from structured to unstructured files, managed and stored in a common platform analyzed through common tooling.

For Volume-IBM has a highly scalable Big Data platform. This includes Netezza and Infosphere groups of products, and Watson-like technologies that can support petabytes volume of data for analytics. But really the support of volume ranges across IBM’s Information Management portfolio both on the database side and the advanced analytics side.

For real time Velocity, we have real time data acquisition. We have a product called IBM Infosphere, part of our Big Data platform, that is specifically built for streaming real time data acquisition and delivery through complex event processing. We have a very rich range of offerings that help clients build a Hadoop environment that can scale.

Our Hadoop platform is the most real time capable of all in the industry. We are differentiated by our sheer breadth, sophistication and functional depth and tooling integrated in our Hadoop platform. We are differentiated by our streaming offering integrated into the Hadoop platform. We also offer a great range of modeling and analysis tools, pretty much more than any other offering in the Big Data space.

Attached- Jim’s slides from Hadoop World

Ajay- Any plans for Mahout for Hadoop

Jim- I cant speak about product plans. We have plans but I cant tell you anything more. We do have a feature in Big Insights called System ML, a library for machine learning.

Ajay- How integral are acquisitions for IBM in the Big Data space (Netezza,Cognos,SPSS etc). Is it true that everything that you have in Big Data is acquired or is the famous IBM R and D contributing here . (see a partial list of IBM acquisitions at at )

Jim- We have developed a lot on our own. We have the deepest R and D of anybody in the industry in all things Big Data.

For example – Watson has Big Insights Hadoop at its core. Apache Hadoop is the heart and soul of Big Data (see ). A great deal that makes Big Insights so differentiated is that not everything that has been built has been built by the Hadoop community.

We have built additions out of the necessity for security, modeling, monitoring, and governance capabilities into BigInsights to make it truly enterprise ready. That is one example of where we have leveraged open source and we have built our own tools and technologies and layered them on top of the open source code.

Yes of course we have done many strategic acquisitions over the last several years related to Big Data Management and we continue to do so. This quarter we have done 3 acquisitions with strong relevance to Big Data. One of them is Vivisimo ( ).

Vivisimo provides federated Big Data discovery, search and profiling capabilities to help you figure out what data is out there,what is relevance of that data to your data science project- to help you answer the question which data should you bring in your Hadoop Cluster.

 We also did Varicent , which is more performance management and we did TeaLeaf , which is a customer experience solution provider where customer experience management and optimization is one of the hot killer apps for Hadoop in the cloud. We have done great many acquisitions that have a clear relevance to Big Data.

Netezza already had a massively parallel analytics database product with an embedded library of models called Netezza Analytics, and in-database capabilties to massively parallelize Map Reduce and other analytics management functions inside the database. In many ways, Netezza provided capabilities similar to that IBM had provided for many years under the Smart Analytics Platform ( ) .

There is a differential between Netezza and ISAS.

ISAS was built predominantly in-house over several years . If you go back a decade ago IBM acquired Ascential Software , a product portfolio that was the heart and soul of IBM InfoSphere Information Manager that is core to our big Data platform. In addition to Netezza, IBM bought SPSS two years back. We already had data mining tools and predictive modeling in the InfoSphere portfolio, but we realized we needed to have the best of breed, SPSS provided that and so IBM acquired them.

 Cognos– We had some BI reporting capabilities in the InfoSphere portfolio that we had built ourselves and also acquired for various degrees from prior acquisitions. But clearly Cognos was one of the best BI vendors , and we were lacking such a rich tool set in our product in visualization and cubing and so for that reason we acquired Cognos.

There is also Unica – which is a marketing campaign optimization which in many ways is a killer app for Hadoop. Projects like that are driving many enterprises.

Ajay- How would you rank order these acquisitions in terms of strategic importance rather than data of acquisition or price paid.

Jim-Think of Big Data as an ecosystem that has components that are fitted to particular functions for data analytics and data management. Is the database the core, or the modeling tool the core, or the governance tools the core, or is the hardware platform the core. Everything is critically important. We would love to hear from you what you think have been most important. Each acquisition has helped play a critical role to build the deepest and broadest solution offering in Big Data. We offer the hardware, software, professional services, the hosting service. I don’t think there is any validity to a rank order system.

Ajay-What are the initiatives regarding open source that Big Data group have done or are planning?

Jim- What we are doing now- We are very much involved with the Apache Hadoop community. We continue to evolve the open source code that everyone leverages.. We have built BigInsights on Apache Hadoop. We have the closest, most up to date in terms of version number to Apache Hadoop ( Hbase,HDFS, Pig etc) of all commercial distributions with our BigInsights 1.4 .

We have an R library integrated with BigInsights . We have a R library integrated with Netezza Analytics. There is support for R Models within the SPSS portfolio. We already have a fair amount of support for R across the portfolio.

Ajay- What are some of the concerns (privacy,security,regulation) that you think can dampen the promise of Big Data.

Jim- There are no showstoppers, there is really a strong momentum. Some of the concerns within the Hadoop space are immaturity of the technology, the immaturity of some of the commercial offerings out there that implement Hadoop, the lack of standardization for formal sense for Hadoop.

There is no Open Standards Body that declares, ratifies the latest version of Mahout, Map Reduce, HDFS etc. There is no industry consensus reference framework for layering these different sub projects. There are no open APIs. There are no certifications or interoperability standards or organizations to certify different vendors interoperability around a common API or framework.

The lack of standardization is troubling in this whole market. That creates risks for users because users are adopting multiple Hadoop products. There are lots of Hadoop deployments in the corporate world built around Apache Hadoop (purely open source). There may be no assurance that these multiple platforms will interoperate seamlessly. That’s a huge issue in terms of just magnifying the risk. And it increases the need for the end user to develop their own custom integrated code if they want to move data between platforms, or move map-reduce jobs between multiple distributions.

Also governance is a consideration. Right now Hadoop is used for high volume ETL on multi structured and unstructured data sources, or Hadoop is used for exploratory sand boxes for data scientists. These are important applications that are a majority of the Hadoop deployments . Some Hadoop deployments are stand alone unstructured data marts for specific applications like sentiment analysis like.

Hadoop is not yet ready for data warehousing. We don’t see a lot of Hadoop being used as an alternative to data warehouses for managing the single version of truth of system or record data. That day will come but there needs to be out there in the marketplace a broader range of data governance mechanisms , master data management, data profiling products that are mature that enterprises can use to make sure their data inside their Hadoop clusters is clean and is the single version of truth. That day has not arrived yet.

One of the great things about IBM’s acquisition of Vivisimo is that a piece of that overall governance picture is discovery and profiling for unstructured data , and that is done very well by Vivisimo for several years.

What we will see is vendors such as IBM will continue to evolve security features inside of our Hadoop platform. We will beef up our data governance capabilities for this new world of Hadoop as the core of Big Data, and we will continue to build up our ability to integrate multiple databases in our Hadoop platform so that customers can use data from a bit of Hadoop,some data from a bit of traditional relational data warehouse, maybe some noSQL technology for different roles within a very complex Big Data environment.

That latter hybrid deployment model is becoming standard across many enterprises for Big Data. A cause for concern is when your Big Data deployment has a bit of Hadoop, bit of noSQL, bit of EDW, bit of in-memory , there are no open standards or frameworks for putting it all together for a unified framework not just for interoperability but also for deployment.

There needs to be a virtualization or abstraction layer for unified access to all these different Big Data platforms by the users/developers writing the queries, by administrators so they can manage data and resources and jobs across all these disparate platforms in a seamless unified way with visual tooling. That grand scenario, the virtualization layer is not there yet in any standard way across the big data market. It will evolve, it may take 5-10 years to evolve but it will evolve.

So, that’s the concern that can dampen some of the enthusiasm for Big Data Analytics.


You can read more about Jim at or

follow him on Twitter at

You can read more about IBM Big Data at

UseR 2012 Early Registration #rstats

Early Registration Deadline Approaches for UseR 2012




  • Early Registration: Jan 23 24 – Feb 29
  • Regular Registration: Mar 1 – May 12
  • Late Registration: May 13 – June 4
  • On-site Registration: June 12 – June 15



Academic Non-Academic Student/Retiree
Registration: Early $290 $440 $145
Registration: Regular $365 $560 $185
Registration: Late $435 $645 $225
Registration: On-site $720 $720 $360
Short Course $200 $300 $100


useR 2012

The 8th International R Users Meeting

Vanderbilt University; Nashville, Tennessee, USA

12th-15th June 2012

Terri Scott & Frank Harrell Reproducible Research with R, LaTeX, & Sweave
Uwe Ligges Writing efficient and parallel code in R
Dirk Eddelbuettel & Romain Francois Introduction to Rcpp
Douglas Bates Fitting and evaluating mixed models using lme4
Jeremiah Rounds RHIPE: R and Hadoop Integrated Programming Environment
Jeffrey Horner Building R Web Applications with Rook
Brandon Whitcher, Jorg Polzehl, & Karsten Tabelow Medical Image Analysis in R
Richard Heiberger & Martin Maechler Emacs Speaks Statistics
Olivia Lau A Crash Course in R Programming
Hadley Wickham Creating effective visualisations
Josh Paulson, JJ Allaire, & Joe Cheng Getting the Most Out of RStudio
Romain Francois & Dirk Eddelbuettel Advanced Rcpp Usage
Terry Therneau Design of the Survival Packages
Martin Morgan Bioconductor for High-Throughput Sequence Analysis
Max Kuhn Predictive Modeling with R and the caret Package
Robert Muenchen Managing Data with R
Barry Rowlingson Geospatial Data in R and Beyond
Karim Chine Cloud Computing for the R environment



Stephania McNeal-Goddard
Assistant to the Chair
Phone: 615.322.2768
Fax: 615.343.4924
Vanderbilt University School of Medicine
Department of Biostatistics
S-2323 Medical Center North
Nashville, TN 37232-2158