SAS and Hortonworks expand Hadoop capabilities
SAS provides data-to-decision business analytics with Hortonworks on Hadoop
CARY, NC (18. Okt. 2013) – SAS and Hortonworks today announce a strategic alliance that will expand SAS® business analytics and data management capabilities on enterprise Apache™ Hadoop ®. With SAS Business Analytics and the Hortonworks Data Platform, organizations can easily incorporate or extend Hadoop as a component to their modern data architecture. This allows businesses to use powerful SAS Analytics across massive data sets, including new data sources that previously could not be captured and analyzed, such as social media, clickstream, IT server log, machine and sensor, and geolocation data.
Until recently Hadoop has been primarily used as a cost-effective, flexible data storage environment for big data. Organizations now aim to apply analytics against these vast data stores to uncover new competitive insights to grow revenue, increase customer satisfaction and strengthen their brand's value.
"Data scientists and business analysts need to easily and quickly explore, visualize and analyze big data stored in Hadoop in an interactive and collaborative environment," said Randy Guard, SAS Vice President of Product Management. "SAS provides domain-specific analytics and data management capabilities natively on Hadoop to reduce data movement and take advantage of Hadoop's distributed computational power. Use of SAS analytics software with Hortonworks Data Platform will help businesses quickly discover and capitalize on new business insights from their Hadoop-based data."
SAS analytics and data management offerings for Hadoop strongly complement Hadoop data management tools, such as MapReduce, Pig, Hive and others, enabling users to uncover more value from their Hadoop-based data in such areas as analytics, metadata management, data lineage and security.
Built and packaged by the core architects of Apache Hadoop, Hortonworks Data Platform (HDP) includes the necessary components to help refine and explore new data sources and find new business insights. As the only 100 percent open-source data management platform for Apache Hadoop, HDP is increasingly becoming a core component of enterprise data management systems as it allows organizations to cost-effectively capture, process and share data in any format and at any scale.
"SAS helps companies around the world unlock the value of their data through advanced analytics. The expanded integration of SAS with Hortonworks Data Platform provides a simple way for customers to broaden their analytic operations across new data sets that can drive smarter business decisions," said Shaun Connolly, Hortonworks Vice President of Corporate Strategy. "Our alliance with SAS extends the availability of 100 percent open-source Apache Hadoop and makes it easy for SAS customers to embrace Hadoop as a core component of their data architecture."
SAS and Hortonworks will continue their investment around Hadoop through increased joint marketing, R&D and customer support. For more details on the SAS and Hortonworks relationship, attend our joint webinar on Nov. 13, 2013, at 10 a.m. PT (1 p.m. ET). This webinar will discuss how organizations are building the modern data architecture for big data analytics with SAS and Hortonworks Data Platform. Register here.
Hortonworks is the only 100 percent open-source software provider to develop, distribute and support an Apache Hadoop platform explicitly architected, built and tested for enterprise-grade deployments. Developed by the original architects, builders and operators of Hadoop, Hortonworks stewards the core and delivers the critical services required by the enterprise to reliably and effectively run Hadoop at scale. Our distribution, Hortonworks Data Platform, provides an open and stable foundation for enterprises and delivers a modern data architecture with the support of key technology partners. Hortonworks also provides unmatched technical support, training and certification programs. For more information, visit hortonworks.com.