Category Archives: Data Science

One User’s Impala Experience at Data Hacking Day

Categories: Data Science Hive Impala

The following guest post comes to you from Alan Gardner of remote database services and consulting company Pythian, who participated in Data Hacking Day (and was on the winning team!) at Cloudera’s offices in February.

Last Feb. 25, just prior to attending Strata, Alex Gorbachev (our CTO) and I had the chance to visit Cloudera’s Palo Alto offices for Data Hacking Day. The goal of the event was to produce something cool that leverages Cloudera Impala

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Cloudera ML: New Open Source Libraries and Tools for Data Scientists

Categories: Community Data Science General Mahout MapReduce Tools

Editor’s note (12/19/2013): Cloudera ML has been merged into the Oryx project. The information below is still valid though.

Last month, Apache Crunch became the fifth project (along with Sqoop, Flume, Bigtop, and MRUnit) to go from Cloudera’s github repository through the Apache Incubator and on to graduate as a top-level project within the Apache Software Foundation. As the founder of the project and a newly minted Apache VP,

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Video Premiere: Training a New Generation of Data Scientists

Categories: Data Science General Training

Data scientists drive data as a platform to answer previously unimaginable questions. These multi-talented data professionals are in demand like never before because they identify or create some of the most exciting and potentially profitable business opportunities across industries. However, a scarcity of existing external talent will require companies of all sizes to find, develop, and train their people with backgrounds in software engineering, statistics, or traditional business intelligence as the next generation of data scientists.

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How-to: Resample from a Large Data Set in Parallel (with R on Hadoop)

Categories: Data Science How-to

UPDATED 20130424: The new RHadoop treats output to Streaming a bit differently, so do.trace=FALSE must be set in the randomForest call.

UPDATED 20130408: Antonio Piccolboni, the author of RHadoop, has improved the code somewhat using his substantially greater experience with R. The most material change is that the latest version of RHadoop can bind multiple calls to keyval correctly.

Internet-scale data sets present a unique challenge to traditional machine-learning techniques,

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