Scalability is the key objective of high-performance software solutions. “Scaling out” is a concept which is accomplished by throwing more server machines at a solution so that multiple processes can run in dedicated environments concurrently. This blog post will briefly touch on several scalability concepts that affect SAS.
SAS In-Memory Statistics for Hadoop is a single interactive programming environment for analytics on Hadoop that integrates analytical data preparation, exploration, modeling and deployment. It’s principle components are the IMSTAT procedure (PROC IMSTAT) and the SAS LASR Analytic Engine (or SASIOLA engine for input-output with LASR). Within the SAS In-Memory Statistics
You may still believe that Hadoop is going to solve all of the world’s problems with big data. It won’t. Hadoop is a framework for storing large-scale data processing with both pros and cons for organizations. Christopher Stevens, from Greenplum, explained that Hadoop is rapidly becoming the go-to for big
Nancy Rausch, from SAS R&D, is driving a short demonstration of how to access Hadoop via SAS Data Integration Studio. Take a look. You're probably going to want to take a look at this paper, too: What's new in SAS Data Management?
sasglobalforum2012 on livestream.com. Broadcast Live Free New this year to SAS Global Forum are Tech Talks. In this session, Chris Hemedinger is chatting with: High-Performance Data Mining Jared Dean, Director of SAS Enterprise Miner R&D Text Analytics and Sentiment Analysis: Case study of AllAnalytics.com Jim Cox, Senior Manager of
Many companies are challenged not only with analyzing big data, but with storing and accessing the data. In some cases, organizations can choose an open source storage solution to reduce costs. One popular open source solution is Hadoop. Anna Brown is talking with Paul Kent, Vice President Big Data at SAS,