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SAS on Your Cluster, Serving your Data (Analysts)

Paul Kent (SAS)
Hadoop Platform Grand Ballroom East
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SAS is a both a Language and an Application for doing Analytics on all manner of data. Recently SAS has adapted to the Hadoop eco-system and intends to be a good citizen amongst the different choices for processing large volumes of data on your cluster. As more people inside an organization want to access and process the accumulated data, the “schema on read” approach degenerates into “redo work someone else might have done already” .
This talk compares and contrasts different data storage strategies, and describes the flexibility provided by SAS to accommodate different approaches. The different techniques are ranked according to convenience, performance, interoperabilty – both practicality and cost of the translation. Techniques considered include:
• Storing the rawdata (weblogs, CSVs)
• Storing Hadoop metadata, then using Hive/Impala/Hawk
• Storing in Hadoop optimized formats (avro, protobufs, RCfile, parquet)
• Storing in Proprietary formats

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Paul Kent


Paul Kent is Vice President of Big Data initiatives at SAS. He spends his time between Customers, Partners and the Research & Development teams discussing, evangelizing and developing software at the confluence of big data and high performance computing. A datacenter rack full of current-generation 64bit x86 processors represents a very large aggregate memory space, thousands of threads and plentiful IO that can be harnessed to solve problems at a much larger scale than we have traditionally been accustomed to.


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