Agenda 

About the Presenters

NANCY RAUSCH

Senior Manager of Software Development, SAS 

Nancy Rausch has been involved for many years in the design and development of SAS’ data warehouse and data management products, working closely with customers and writing a number of papers on best practice design principles for data management solutions. She holds an MS from Duke University in computer engineering, where she specialized in statistical signal processing, and a BS in electrical engineering from Michigan Technological University. She has recently returned to college after many years, and is currently pursuing an MS in analytics from Capella University.

The Architecture of the SAS® Cloud Analytic Services in SAS® Viya™*

SAS Cloud Analytic Services (CAS) is the cloud-based run-time environment for data management and analytics in SAS. By run-time environment, we refer to the combination of hardware and software where data management and analytics take place. In a sense, CAS is just another SAS platform to do things. CAS is a platform for high-performance analytics and distributed computing. The CAS server provides data management and an analytics framework that can run in the cloud, that can act as a cloud, and that provides the best-in-class analytics that SAS is known for. This new architecture functions as a public API, allowing access from many different clients such as Lua, Python, Java, REST, and yes, even SAS. The CAS server is designed to provide user-level sessions, to share data between sessions, and to provide fault tolerance, which allows a worker node to crash without losing data and allows the user action to continue running to completion. The isolation provided to each session allows one session to crash without affecting other sessions. The concept of “always in memory” in CAS means that an action is not aware of what the server does to allow the action to access the data. The entire file might be in memory or just pieces of the file might be mapped into memory, just in time for the action to access the data. This allows CAS tables to be loaded that are larger than the memory available across the grid. Hadoop can be used to provide data redundancy. The server is elastic and can add or remove nodes as needed. Users can specify how many nodes they want their session to use, so that the session fits their needs.

What's New in SAS® Data Management

The latest releases of SAS® Data Management software provide a comprehensive and integrated set of capabilities for collecting, transforming, and managing your data. The latest features in the product suite include capabilities for working with data from a wide variety of environments and types including Hadoop, cloud data sources, RDBMS, files, unstructured data, streaming, and others, and the ability to perform ETL and ELT transformations in diverse runtime environments including SAS®, database systems, Hadoop, Spark, SAS® Analytics, cloud, and data virtualization environments. There are also new capabilities for lineage, impact analysis, clustering, and other data governance features for enhancements to master data and support metadata management. This paper provides an overview of the latest features of the SAS® Data Management product suite and includes use cases and examples for leveraging product capabilities.

 NICOLE ACKERMANN

Statistical Data Analyst, Washington University School of Medicine 

Nicole Ackermann is a statistical data analyst at Washington University School of Medicine, Division of Public Health Sciences. Nicole has been working as a data analyst at Washington University for 3 years. She holds bachelors’ degrees in biology and political science and obtained her Master’s in Public Health, with a focus in Epidemiology, from Saint Louis University in 2015. Nicole began using SAS in her master’s program and has been using SAS consistently for almost 4 years, learning something new just about every day.

Troubles with Text Data

Data obtained via text message is becoming increasingly popular in health research. This presentation will focus on managing text survey data in a randomized control trial in which participants complete four weekly text surveys. Text data was captured on the cloud communications platform, Twilio. SAS® was used as the main data management software and throughout the data management process, many useful SAS® tips and tricks were discovered, including several functions that may be helpful to the average SAS® user. (Work supported by National Cancer Institute Grant: R01CA19039104-A)

 KATHY BERRI

Software Engineer, Enterprise Holdings, Inc. 

Kathy Berri is a Software Engineer at Enterprise Holdings, Inc. She has a BS in Systems and Data Processing from Washington University and has been a SAS user for the past 4 years.  She works with the EHI Marketing team to help in marketing to customers of Enterprise, Alamo and National thru email campaigns.  She uses SAS as a method to interact with the company’s Teradata Enterprise Data Warehouse, transfer of files to and from vendors, and for generating reports.  Kathy’s knowledge of SAS is self-taught by replicating code that exists, using Google, and reading some old books passed down to her.  She is interested in this group to help learn more about the world of SAS Users and its capabilities. 

USING Teradata and SAS to Market to Customers

Bruce Schaalje

Statistician, Data Science and Analytics, Monsanto Technology Development

Bruce Schaalje is a statistician in the Data Science and Analytics team of Monsanto’s Technology Development organization. He has worked for Monsanto for 2 years. Prior to that he was professor of statistics at Brigham Young University for 24 years, before which he worked as a research scientist for Agriculture Canada. He holds a Ph.D. in Statistics and Biomathematics from North Carolina State University as well as Bachelors and Masters Degrees from Brigham Young University and the University of Washington. He has been an enthusiastic SAS user for 40 years.

Literate Programming Using SAS

Regulatory statisticians have a difficult job. They work in a high-production environment with aggressive deadlines, and their reports are subject to stringent verification standards with multiple layers of quality control. Much of the work is repetitive, involving similar data sets and multiple revisions of reports. However, it must be done with statistical rigor and meticulous attention to detail. Literate programming, which weaves together statistical analysis code and report generation code, is beneficial in this environment. SAS has extensive literate programming capabilities.

DAJUN TIAN

Performance Research Analyst, BJC HealthCare 

Dajun Tian is currently a performance research analyst at BJC HealthCare. He has worked in the biomedical and healthcare industries with a focus on real world data. He holds a Master of Science degree in biostatistics from Washington University in St. Louis and uses SAS to analyze patient experience survey data and electronic medical records.

Perl Regular Expressions in SAS: A Hands-on Tutorial