SAS® has customers in 140 countries and more than 70,000 customer sites.
Bringing the power of SAS to Hadoop to get better answers, faster. For many organizations, Apache™ Hadoop® is used as a sandbox to dump data of uncertain value that is too unwieldy to store in a traditional relational database. The data can be loaded into Hadoop, and then aggregated, filtered and structured to see if there is any value to be gleaned from it. In stark contrast, some estimates anticipate half the world’s data will be processed by Hadoop within five years.
From big data proof-of-concepts to enterprise-wide deployments, it’s critical to pick your vendor partners wisely to ensure initial success at the small scale doesn’t deteriorate into frustrating live operations. With this in mind, SAS and MapR have combined their respective strengths to ensure your big data aspirations are met, whatever your project stage and scale-out growth plans.
SAS and the MapR Distribution including Apache Hadoop are natural complements. Despite the many technical nuances between various Apache subprojects and Hadoop-based capabilities, SAS support for Hadoop can be boiled down to two simple ideas: Hadoop data can be leveraged using SAS; and the power of SAS Analytics has been extended to Hadoop. By partnering with the market-leading MapR Distribution for Hadoop, SAS applications can now liberate the information gems you seek from the big data tsunami sweeping through your organization. MapR is the only enterprise-grade distribution of Hadoop that is easy, dependable and fast for real-time production workloads.
Just as with other data sources, data stored in MapR clusters can be consumed across the SAS software stack in a transparent fashion. This means that analytic tools already in place (such as SAS Enterprise Miner™), tools relating to data management (such as SAS Data Integration Studio), and foundation tools (such as Base SAS) can be used to work with Hadoop data. What you have can become even more valuable with the addition of SAS Hadoop capabilities. To this end, SAS has developed a number of initiatives to enable SAS users to access, load, process, visualize and analyze data stored in Hadoop. These efforts are focused on three key areas: bringing the power of SAS Analytics to Hadoop with easy data access; taking advantage of Hadoop’s distributed processing from within SAS, and bringing governance to Hadoop implementations using SAS Information.
MapR provides several key advantages to make analytics professionals more productive. MapR snapshots and volumes allow users to build and test models on the same cluster for production data without impacting operations. This also allows for easy versioning of models and back-testing against historical data sets. In addition, unlike other distributions for Hadoop, only MapR provide a fully read-write data platform which allows existing applications, custom libraries, and modeling languages, and scripts (e.g., Grep, Git) to work out of the box. Moreover, data movement is quick and easy with MapR Direct Access NFS™ without requiring a separate cluster for data ingest.
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.
MapR delivers on the promise of Hadoop with a proven, enterprise-grade platform that supports a broad set of mission-critical and real-time production uses. MapR brings unprecedented dependability, ease-of-use and world-record speed to Hadoop, NoSQL, database and streaming applications in one unified distribution for Hadoop.
Get the MapR Sandbox for Hadoop, a fully functional Hadoop cluster running on a virtual machine. Visit mapr.com/sandbox
Take the next step with SAS
Take the Next Steps with SAS. Visit www.sas.com/en_us/software/ sas-hadoop.html and then engage with your SAS account team to move ahead.