Bridging the Gap Between Legacy SAS® and SAS® Viya® at the Centers for Medicare and Medicaid (CMS)
- Article History
- RSS Feed
- Mark as New
- Mark as Read
- Bookmark
- Subscribe
- Printer Friendly Page
- Report Inappropriate Content
Rick Andrews (CMS); Manuel Figallo, SAS Institute; Kevin Boone, SAS Institute; Prakash Subramanian, SAS Institute; Logan Perry, SAS Institute
Abstract
SAS Viya has introduced many powerful capabilities to enrich the end user experience. From scalable infrastructure resources to easy-to-use front-end features, SAS Viya can improve a team or organization’s ability to turn data into insights. Having decided to evaluate or move to SAS Viya, end-users are often left wondering how they can “bridge the gap” between their legacy investment in SAS to the more Cloud-native SAS Viya version.
Based on the experiences of such users at CMS, this paper helps others facilitate a move to SAS Viya. Of particular interest are those end-users who:
- Currently use client-server or mainframe applications --such as SAS Enterprise Guide, SAS Stored Processes, and SAS for the IBM® mainframe-- for querying or coding and want to bridge the gap with their SAS Viya equivalents, i.e., SAS Studio and SAS Job Execution.
- Use Open Source (Python) and want to bridge the gap between their Python notebook environment and SAS Viya CAS using Python SWAT (Scripting Wrapper for Analytics Transfer).
Watch the presentation
Watch Bridging the Gap Between Legacy SAS® and SAS® Viya® at the Centers for Medicare and Medicaid (CMS) as presented by the authors on the SAS Users YouTube channel.
Introduction
Like many government agencies, CMS is utilizing more Cloud-based services. SAS Viya is built to be scalable for both private and public Clouds. Complex analytical, in-memory calculations are optimized, because SAS Viya scales compute capacity to faster process increased workloads using available infrastructure resources. This lets an analyst to quickly experiment with different scenarios and apply more sophisticated approaches, such as machine learning, to increasingly large volumes of incoming data.
Figure 1: "Bridging the Gap" at CMS Involves Migration and Integration.
The remainder of the paper can be found in the attached pdf.