Meet peak computing demands reliably and cost-effectively.
Balance your workload.
SAS Grid Manager gives IT the flexibility to meet service level commitments by easily reassigning computing resources to meet peak workloads or changing business demands. A central point of control lets you easily administer policies, programs, queues and job prioritization across users and applications to achieve business goals under a given set of constraints.
Be ready for anything with a highly available SAS computing environment.
Having multiple servers in a grid computing environment enables jobs to run on the best available resource. If a server fails, its jobs can be transitioned seamlessly to another server. IT staff can also perform maintenance on specific servers, as well as introduce additional computing resources, without interrupting analytics jobs or disrupting the business. And because analysts in today's increasingly diverse analytics ecosystems are using a variety of programming languages, SAS Grid Manager lets you manage all your jobs, in SAS and other languages, ensuring all your analytics run quickly.
Divide and conquer.
Multiprocessing capabilities let you divide individual jobs into subtasks that run in parallel, reducing processing time. This is particularly effective for analytics programs with large data sets and long run times, as well as those with repetitive runs of independent tasks running against large data sets. You can take advantage of all available computing resources now and cost-effectively scale out as needed, adding capacity in single processing units with commodity hardware. There’s no need to size today’s environment for anticipated future needs.
Get to Know SAS® Grid Manager
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- Webinar SAS Grid Manager and Kerberos AuthenticationLearn the considerations for implementing Kerberos authentication so you can submit workloads to SAS Grid Manager.
- Webinar SAS Grid Manager and SAS Viya: A Perfect PairLearn how SAS Grid Manager works with SAS Viya to process large volumes of data for rapid results.
- Webinar The Total Economic Impact of SAS Grid Manager Learn the results of a Forrester Total Economic Impact study that examined the potential ROI of a current SAS Grid Manager customer.
- White Paper SAS® Grid Computing – What They Didn’t Tell YouJoin Austria’s Erste Group Bank on their journey from a monolithic SAS processing environment to a more flexible infrastructure using SAS Grid Manager software.
- E-Book SAS Grid Computing For Dummies, Second EditionBy implementing a SAS Grid Computing environment, you can better balance and manage workloads, use resources to their full extent, and increase productivity and overall performance. This e-book tells you how.
- White Paper Outcompete by Outcomputing With SAS Grid Manager on Red Hat Enterprise LinuxLearn how SAS Grid Manager with Red Hat Enterprise Linux configurations demonstrate robust performance and a scalable environment using commodity hardware and a wide variety of shared file systems.
- Customer Story Achieving academic and operational excellence through business intelligenceCurtin University uses SAS Visual Analytics to provide reporting across the organization.
- White Paper The Total Economic Impact™ Of SAS Grid ManagerRead this Forrester Total Economic Impact (TEI) report to learn more about the potential ROI, benefits, savings and flexibility you can gain when you invest in SAS Grid Manager.
- Customer Story Building materials leader optimizes production using analyticsUSG Corporation relies on SAS to improve its manufacturing process and reduce downtime, costs and energy consumption.
- Customer Story Ensuring mental health patient safety during a global pandemicSAS delivers dashboard to monitor COVID-19 in the Copenhagen Regional Psychiatric Centers.
Check out these grid-enabled SAS products and solutions.
- SAS® Enterprise Guide®Access powerful analytics and reporting from a point-and-click, menu- and wizard-driven tool.
- SAS® Enterprise Miner™Streamline the data mining process to create highly accurate predictive and descriptive models based on large volumes of data.