César Pérez



Big data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions. With big data analytics, data scientists and others can analyze huge volumes of data that conventional analytics and business intelligence solutions can't touch. Consider this; it's possible that your organization could accumulate (if it hasn't already) billions of rows of data with hundreds of millions of data combinations in multiple data stores and abundant formats. High-performance analytics is necessary to process that much data in order to figure out what's important and what isn't. Using big data analytics you can extract only the relevant information from terabytes, petabytes and exabytes, and analyze it to transform your business decisions for the future. Becoming proactive with big data analytics isn't a one-time endeavor; it is more of a culture change – a new way of gaining ground by freeing your analysts and decision makers to meet the future with sound knowledge and insight. SAS support for big data implementations, including Hadoop. throught SAS and Hadoop is possible work in all steps of Analytical Process: Identify/formulate Problem, Data Preparation, Data Exploration, Transform and select, Buil Model, Validate model, Deploy Model and Evaluate/Monitor Results. This book presents the work possibilities that SAS offers in the modern sectors of big data, Business Intelligence and Analytics. The most important tools of SAS are presented for processing and analyzing large volumes of data in an orderly manner. In turn, these tools allow also extract the knowledge contained in the data.