SAS® Analytics Accelerator for Teradata

In-database analytics improve data governance on predictive analytics projects and produce faster, better results

SAS Analytics Accelerator for Teradata enables the execution of key SAS analytical, data discovery and data summarization tasks within a Teradata database or data warehouse.

This type of in-database processing reduces the time needed to build, execute and deploy powerful predictive models. It also increases the utilization of the enterprise data warehouse or relational database to reduce costs and improve the data governance that is required for successful analytic applications.

Benefits

Reduce data movement and redundancy to ensure data quality and enhance resource use.

In-database analytics reduce, or eliminate, the need to move massive data sets between a data warehouse and the SAS environment or other analytical data marts for multipass data preparation and compute-intensive analytics.

Improve accuracy and achieve better outcomes using more data points and sophisticated analytical models.

The massively parallel architecture of data warehouses is useful for processing larger, more complex information sets. Modelers can easily add new sets of variables if model performance degrades or changes are needed for business reasons.

Achieve faster time to results by building, updating and deploying models more quickly.

SAS Analytics Accelerator for Teradata enables analytical processing to be pushed down to the database or data warehouse, shortening the time needed to build and deploy predictive models. It also reduces the latency and complexity associated with the model development process. Analytics professionals have fast access to up-to-date, consistent data and increased processing power. This delivers faster time to results and provides better insights for improved business decision making.

Enhance the productivity of analytic teams.

In-database analytics helps modelers, data miners and analysts focus on developing high-value modeling tasks instead of spending time consolidating and preparing data.

Features

  • Statistical and analytical functions enabled for in-database processing
  • SQL generation options specify the type of in-database computing

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