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Big Data Analytics with SAS

Big Data Analytics with SAS

By : Pope
5 (8)
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Big Data Analytics with SAS

Big Data Analytics with SAS

5 (8)
By: Pope

Overview of this book

SAS has been recognized by Money Magazine and Payscale as one of the top business skills to learn in order to advance one’s career. Through innovative data management, analytics, and business intelligence software and services, SAS helps customers solve their business problems by allowing them to make better decisions faster. This book introduces the reader to the SAS and how they can use SAS to perform efficient analysis on any size data, including Big Data. The reader will learn how to prepare data for analysis, perform predictive, forecasting, and optimization analysis and then deploy or report on the results of these analyses. While performing the coding examples within this book the reader will learn how to use the web browser based SAS Studio and iPython Jupyter Notebook interfaces for working with SAS. Finally, the reader will learn how SAS’s architecture is engineered and designed to scale up and/or out and be combined with the open source offerings such as Hadoop, Python, and R. By the end of this book, you will be able to clearly understand how you can efficiently analyze Big Data using SAS.
Table of Contents (17 chapters)
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Architecture


While working with small data such as the size of data that fits on a desktop or laptop, processing speed is impacted by several factors, such as the speed of the processor or processors, the amount of memory, and the size of the usable disk space. While it is important to optimize the resources at the desktop level to ensure the best response times and the amount of work that can be done, it becomes vital in working with big data to have the proper analytic processing environment setup using the proper configurations and options.

This will ensure a team of architects, data scientists, and business analysts can prepare, analyze, and most importantly, actually put the results of analytics into production as fast as necessary to gain value or provide a competitive advantage.  

It is the overall enterprise architecture that allows for big data analytics to process efficiently and to succeed in providing value, or else it becomes a bottleneck that needs to fixed.  Analytical insights...

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