≡ Menu

First Look: Teradata Appliance for SAS, Model 750


SAS has been focusing on in-memory analytics recently with its new Visual Analytics products for instance. Teradata and SAS have been working together to enable these in-memory products for Teradata customers and today announced a new appliance. The expansion to the Teradata Appliance for SAS, Model 750 now supports SAS High Performance Analytics (HPA) Products, Visual Analytics, Visual Statistics and IMSTAT (in-memory STAT).

The Model 750 uses new Intel chips for faster computation and adds more memory options – 256 GB 512GB or 768GB per node. Scalability has improved also with support for clusters of over 600 nodes. SAS Managed Server nodes can be included also to run traditional SAS products that don’t have a specific in-memory version.

In the new appliance, data for these in-memory products is loaded directly from the Teradata warehouse into memory without any duplication. Because the Model 750 sits in the Teradata Unified Data Architecture and connects to Teradata BYNET using Infiniband, all the data available inside the Teradata UDA can be processed in-memory using the appliance, regardless of where it is stored (data warehouse, discovery platform, Hadoop etc).

The Model 750 also provides highly parallelized access to data so that hundreds of streams can be processed in parallel. The in-memory analytic processing works without imposing any costs on the rest of the Teradata infrastructure, allowing high performance analytics to be executed against the same data involved in typical SQL transaction processing.

SAS and Teradata have over 200 customers in common. The combination of the new appliance and in-memory software can reduce the time run complex analytics dramatically. Because the whole environment is integrated into Teradata BYNET, data governance and security is easier and the in-memory analytics are integrated directly into the data architecture.

There’s more information on the Teradata/SAS appliances here.


Comments on this entry are closed.