Название: A Practitioners' Guide to SAS: Mastering SAS Consulting: Design, Deploy, and Manage Analytics Environments
Автор: Anirban Dey
Издательство: Apress
Год: 2026
Страниц: 597
Язык: английский
Формат: epub (true), pdf
Размер: 10.1 MB

The SAS ecosystem is evolving. Whether you work with SAS Viya or SAS 9, there's never been a better time to dive in and master its full potential. This book guides you through the technologies and environments that make SAS a powerful asset in data analytics, revealing the surprising depth and rich capabilities that SAS offers.

This comprehensive guide is tailored for consultants and operations teams, covering everything from strategic analysis and architecture to implementation, administration, and best practices. You'll explore the analytics landscape across both commercial and open-source systems, learning how to design, deploy, and manage SAS environments effectively.

By the end of this book, you'll be equipped to confidently implement and operate SAS technology turning raw data into real insights and business value.

Historically, SAS environments were on-premise, batch-driven, and highly centralized. Organizations invested heavily in stable infrastructures running SAS 9.x platforms, often integrated with data warehouses and governed by strict IT controls. These environments were robust—but rigid.
Today, the analytics landscape has shifted dramatically:

• Cloud-native architectures have redefined scalability.
• Open-source ecosystems such as Python and R have become mainstream.
• Distributed computing frameworks now handle massive data volumes.
• Real-time analytics and AI are expected, not optional.
• Governance and compliance demands are increasing globally.

• R: A statistical computing and graphics language that is particularly popular in academia and among statisticians. R’s large repository of packages makes it a versatile tool for statistical analysis, predictive modeling, and data visualization. With its growing ecosystem and vibrant community, it competes directly with SAS in terms of statistical analysis.
• Python: Python, with libraries like Pandas, NumPy, SciPy, Matplotlib, Scikit-learn, and TensorFlow, has gained widespread adoption in Data Science and Machine Learning. Python’s simplicity, combined with its powerful data manipulation and analysis capabilities, makes it a strong competitor of SAS, especially in the realm of Machine Learning and AI. The growth of Jupyter Notebooks for data exploration also contributes to Python’s popularity.

What You Will Learn:
· Understand Consulting, Operations and Process activities associated with SAS technologies
· Support SAS data sourcing and reporting deliveries
· Establish best practices and guidelines for usage of the SAS platform

Who This Book Is For:
Consultants, Administrators, Architects, and Developers.