The paper is written for those who wants to learn the enterprise-level transition from SAS to open-source programming. The paper will introduce the transition project that the whole department of 150+ SAS programmers has completely moved from SAS to Open-source programming. The paper will start with the scopes of the project – Analytic platform switch from SAS Studio to R Pro Server, converting the existing SAS codes to R/Python codes, Window server to AWS Cloud computing environment, and the transition of SAS programmers to R/Python programmers. It will also discuss the challenges of the project such as inexperience in Open-source Programming, new analytic platform, and change management. The paper will introduce how the transition-support team, executive leadership and SAS programmers have overcome the challenges together during the project. The paper will also discuss the difference in SAS and Open-source language and programming, and it will show some examples of the conversion of SAS codes to R/Python codes. Finally, it will close with the benefits of the Open-source programming transition and the lessons learned from the project.

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Enterprise-levelTransition from SAS
to Open-Source Programing for the
whole department
Kevin Lee
Disclaimer
The views and opinions presented here represent those of the
speaker and should not be considered to represent any
companies or organizations.
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• Scope of Open-Source Transition
Project
• Challenges of Open-Source
Transition Project
• How to overcome Challenges
• Difference between SAS and
Open-source Programming
• Change Management by
Leadership
• Benefit of Open-Source
Transition Project
• Lessons Learned
• Conclusion
Open-Source Programming ( R & Python) Adoption by Data
Scientist has increased from 35% in 2014 to 77% in 2020 based on
Burtch Works Survey.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
2013 2014 2015 2016 2017 2018 2019 2020 2021
SAS
Open Source
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How has we, as individuals, transitioned/expanded
from SAS to Open-Source Programmer?
• Learned New Programming Language – R or/and Python
• Learned New Analytical System – R Studio / Jupyter
• Converted previous SAS codes to new Open-source codes
• Adjusted to new culture in Open-source (no dedicated customer
service from SAS)
What about the entire department
(150 SAS programmers)?
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High Level Infrastructure Transition
from SAS Window to AWS Open-SourceAnalytical System
Window Server
SAS Studio
Oracle Database
AWS Cloud Computing
R Studio Pro Server
Redshift Data Warehouse
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Scope of Open-Source Transition Project
SAS Programming
SAS Studio
Window Server
SAS programming
SAS Programmers(150)
SAS codes(230)
Oracle Relational Database
Open-Source Programming
R Pro Server ( R Studio, Jupyter)
AWS Cloud Computing(Linux)
Open-Source Programming ( R,
Python, SQL)
Data Scientists
R, Python & SQL codes
AWS Redshift Data Warehouse
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New AWS
Open-Source
Analytical
System
AWS Cloud
Redshift
Data
Wareho
use
S3
EC2/
Linux
R
Studio
Jupyter
SQL
Workbe
nch
Git
Tableau
Difference between SAS and Open-Source Programming
SAS Programming Open-Source Programming
Analytic Interface SAS Studio R Studio, Jupyter, SQL Workbench
Data Processing Row by Row Column by Column
comments * #
Processing Structure DATA / PROC steps Single statement
Function Macros function
Macro variables Yes No (at least, I have not found one yet)
Data Structure SAS datasets Data Frame
Object-oriented No Yes
Customer Support Dedicated Support Community Support
Feature Update Less Frequent Very frequent since new features could be
developed by anyone.
Speed High Level Language, so fast SQL & Python are very fast – more suitable
for Big Data
System Closed System, thoroughly validated Open System, transparent but validation
could be questioned
Machine Learning At early stage The most popular language
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Difference between SAS and Open Source
studyid subjid siteid age sexc
• SAS process the data a row by a row.
• R/Python/SQL process the data a column by a column.
studyid subjid siteid age sexc sex
studyid subjid siteid age sexc sexc sex • Less memory to use
• Faster to process
• Difficult to process/create
multiple variables
data DM;
set DEMO;
if SEXC = ‘Male’ then SEX = ‘M’
else if SEXC = ‘Female’ then SEX = ‘F’
else if SEXC = ‘Unknown’ then SEX = ‘U’
run;
DM[‘SEX’] = DM.SEXC.replace([‘Male’,’Female’,’Unkonwn’ ], [‘M’, ‘F’, ‘U’])
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Challenges for Open-Source Transition project
• Inexperience in Open-Source Programming
(e.g., R, Python)
• Inexperience in new system / environment (
AWS Cloud Computing, R Studio, Jupyter,
Linux )
• Learning curve
• Daily works as well as existing SAS codes
conversion
• Lack of enterprise customer support on
Open-Source Programming
• Version control
• Uncertainty toward new culture created by
Open-Source programming
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How to Overcome the Challenges
• Training on Open-Source programming
(R & Python)
• Continuous Workshop on the new
analytical system
• R Pro Server ( R Studio, Jupyter )
• Redshift Data Warehouse
• Linux
• S3
• SAS codes conversion (+230) to
R/Python/SQL by Transition Support
Team
• Change Management by Leadership
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Sprint Cycle of SAS Codes Conversion
• Conversion for
Team 1 (40 codes)
• UAT 1
• SOP 1
Sprint 1
• Conversion for
Team 2 (50 codes)
• UAT 2
• SOP 2
Sprint 2 • Conversion for
Team 3 (50 codes)
• UAT 3
• SOP 3
Sprint 3
• Conversion for
Team 4 ( 50
codes)
• UAT 4
• SOP 4
Sprint 4
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Change Management by Leadership
• Clear goals, plan and timeline of the
change (Open-source programming)
• Executive Support
• Resources and Budgets for a change
• Continuous Support (e.g., new
programming, system, infrastructure)
during the transition from SAS to Open-
source.
• Dedicated Supporting Team /SME
• Frequent, transparent communication
on the project progress/update
• Full Participation from the whole team
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Benefits for Open-Source Programming Transition
• Cultural Changes
• Less dependent on tools (Enterprise
System)
• Proactive
• Open to new technologies
• Faster to adopt new changes
• Collaborative
• More Opportunities
• To learn new languages (R, Python)
and system (Cloud Computing)
• To keep top talents
• Cost effective
• Innovative, Advanced Analytics (Big
Data, Data Visualization, Machin Learning)
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Lessons Learned from Open-Source Transition Project
• Support for programmers during the
Transition is critical.
• Training
• Workshop
• Dedicated Supporting Team during the
Transition is critical.
• Know-how / Experience on Existing
System and Open-Source System is critical
• Change Management by Leadership is the
key to success.
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Conclusion
• Successful Transition from SAS Programming to Open-Source Programming in
enterprise level (e.g., Biometric Department) is more than learning new
languages.
• Successful Transition from SAS Programming to Open-Source Programming in
enterprise level requires for the whole team, especially from the
top(leadership).
• Cultural Change toward more open, proactive, advanced analytic environment
(Data Visualization, Machine Learning, Big Data).
• Open-source programming Transition Project has provided More
opportunities for SAS programmer to learn the new technologies.
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Any Questions?
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