Let’s be honest. When well planned, a SAS Grid Computing platform as the basis for a shared, highly available, high-performance analytics environment can pay for itself many times over. However, it is critical that your overall objectives and computing environment be well understood for you to achieve success with your SAS Grid implementation and to get the maximum benefit.
This post is the first in a series that will explore some of the best practices in setting up a high-performance, high-availability SAS analytics environment, but first let’s take time to understand what you can expect from a grid implementation:
When to say yes to the grid
- Your SAS applications are mission critical, and you need to set up a highly available infrastructure.
- You have lots of SAS users running lots of SAS applications, and you want to implement a shared SAS Analytic environment that allocates resources as needed.
- You have end-of-month or end-of-quarter SAS processing that has very tight SLAs, and you need to be guaranteed that your compute resources meet these SLAs.
- You would like to establish a hardware and software infrastructure that can be scaled out to meet your ever-growing SAS user base and the ever-growing data being analyzed by these SAS users.
- You would like to see if you can gain some performance improvements by utilizing the new SAS High Performance Analytics processes or take your existing SAS processes and convert them to a distributed processing format or both. Please note that not all SAS processes are good candidates for distributed processing. For example, processes that rely heavily on OLAP processing do not lend themselves to parallelization.
Here are some papers that you can read to learn more on the above: