Prevent delays resulting from slow analytical processing. And capture timely insights from big data so you can seize opportunities you would otherwise miss.

Boost model performance.

Automate data-scoring processes for faster insights. Publish analytical models into database-specific functions that are deployed and executed directly within the database environment.

Increase data mining and IT productivity.

Faster deployment frees your analytics team to focus on new projects. By reducing the need to manually revalidate code for your models, you'll incur fewer labor costs.

Streamline analytic deployment.

There's no need to move data between SAS and the database for scoring purposes. This reduces the cost, complexity and latency of the scoring process. And it streamlines your analytic deployment.

Reduce data movement.

Reducing data movement and replication ensures data integrity, which is crucial for data governance. By scoring data inside the database, you can execute programs so that detail data is analyzed without crossing the database boundary.

Key features

Eliminate inconsistencies caused by excessive data movement and latency from conventional processes for scoring data.

SAS format library

This XML file is deployed once to the database system.

SAS Enterprise Miner Score Export node

Lets you export the model into files that are ready to deploy to the database for scoring.

Register SAS/STAT linear models to SAS Model Manager

Supports in-database scoring for SAS/STAT models.

Publishing client

Pushes the scoring function into the database. Once inside, a SQL expression is used to execute the score.

Integrated environment for tracking & monitoring model performance over time

Further streamlines the registration and validation of vendor-defined functions (VDFs) inside the database.

Available for multiple data sources, including Hadoop & Spark

See the Features List for a complete list of supported databases.

SAS Viya models

Select models created by submitting a CAS action or a SAS procedure, or by using Model Studio, can be deployed in-database.