|By Business Wire||
|March 24, 2014 01:01 PM EDT||
The new offering enables multiple users to simultaneously and interactively manage, explore and analyze data, build and compare models, and score massive amounts of data in Hadoop. Its interactive analytics programming environment for Hadoop is based on SAS in-memory technology.
By enabling enterprises to draw deep insights from big data assets faster and with greater precision, SAS In-Memory Statistics for Hadoop boosts the bottom line, reduces risk, improves customer understanding and increases opportunities for success.
“We look forward to working with SAS on SAS In-Memory Statistics for Hadoop serving as the recommendation engine in our surprize customer loyalty program,” said Herbert Bucheli, Head of Business Analytics Services at the Aduno Group, a Swiss cashless payment service provider.
“Customers in our loyalty program accrue credits for using our services and may use those towards other purchases. The new SAS software’s recommendation engine will quickly build personalized offers for each customer based on history within our big data repository. SAS In-Memory Statistics for Hadoop is expected to be a good fit for this project,” added Bucheli.
The SAS in-memory architecture offers unprecedented speed – an absolute requirement for finding value in massive amounts of data. The same in-memory analytics technology that powers the popular SAS Visual Analytics data visualization software also supports SAS In-Memory Statistics for Hadoop.
SAS In-Memory Statistics for Hadoop supports numerous statistical and machine learning modeling techniques, including: clustering, regression, generalized linear models, analysis of variance, decision trees, random decision forests, text analytics and recommendation systems.
“Many of our customers are turning to Hadoop as their big data architecture. With innovative products like SAS In-Memory Statistics for Hadoop, SAS is becoming the choice for data management and analytics to draw value from Hadoop and the volumes of big data organizations are accumulating,” said Wayne Thompson, SAS Chief Data Scientist.
“SAS In-Memory Statistics for Hadoop supports a wide range of analytical activities, while taking advantage of our in-memory technology to get blazing fast results,” said Thompson.
Industry analyst firm IDC expects Hadoop to reach $812.8 million in sales in 2016 – a compound annual growth of 60.2 percent. SAS anticipates customers will similarly continue deploying big data architecture to glean big insights.
Hadoop spreads data over large clusters of commodity servers and performs processes in parallel. It also detects and handles failures, which is critical for distributed processing. In addition to low distributed hardware cost and the safety net of data redundancy, Hadoop’s notable advantages include:
- Parallel processing. Hadoop’s distributed computing model can process huge volumes of data.
- Scalability. Hadoop systems can be grown easily by adding more nodes.
- Storage flexibility. Unlike traditional relational databases, data does not need to be preprocessed for storage, and Hadoop easily stores unstructured data.
Today's announcement was made at SAS Global Forum, the world's largest gathering of SAS users, attended by more than 4,500 business and IT users of SAS software and solutions. Select sessions and keynotes from the user conference will be livestreamed.
SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW®.
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2014 SAS Institute Inc. All rights reserved.
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