We are surrounded by massive quantities of data, and somewhere in there is the information that your organization needs. But being able to perform complex analysis on huge amounts of data isn’t enough – the analysis needs to take place quickly enough for you to be able to act on the results. High-performance analytics from SAS provides the ability to rapidly perform complex analysis on big data, enabling you to solve problems that you thought were unsolvable.
SAS uses three main technologies for high-performance analytics: in-memory analytics, in-database analytics, and grid computing. Documentation from SAS Publishing provides the information you need, no matter which technology you use.
- SAS Visual Analytics provides a drag-and-drop Web interface to enable you to quickly explore huge amounts of data. You can spot patterns and opportunities for further analysis through visual reports that you can view on the Web or on a mobile device, such as an Apple iPad. SAS Visual Analytics 5.1: User’s Guide provides complete information to guide you through preparing and exploring data and designing and viewing reports. A key component of SAS Visual Analytics is the SAS LASR Analytic Server, which reads data into memory for analytic processing at a staggering rate, such as a billion rows in four seconds. We provide complete documentation for SAS LASR Analytic Server to customers.
- SAS High-Performance Analytics is a product that provides a set of SAS procedures that perform in-depth analysis, including predictive modeling, on a high-performance computer system. SAS High-Performance Analytics: User’s Guide provides customers with complete programming information and examples for these procedures.
- Some SAS operations can run as functions inside of other software vendors’ relational database code, which reduces the time required to access and analyze the data. SAS 9.3 In-Database Products: User’s Guide provides information about the functions that run inside of a database, including the use of SAS Scoring Accelerator and SAS Analytics Accelerator. Some of the databases include Teradata, Oracle, IBM DB2, EMC Greenplum, Netezza, and Aster nCluster.
- Grid computing dynamically distributes large computing tasks among multiple computers on a network to provide parallel processing at the task level. Using SAS Grid Manager is a cost-effective way to make SAS scalable and highly available, provide workload balancing, and enable many concurrent users. Use Grid Computing in SAS 9.3 to understand more about grid computing and how to use a grid to improve the speed and efficiency of your SAS programs and processes.