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Enhance your SAS data-wrangling skills with high-precision and parallel data manipulation using the DS2 programming language. Now in its second edition, this book addresses the DS2 programming language from SAS, which combines the precise procedural power and control of the Base SAS DATA step language with the simplicity and flexibility of SQL. DS2 provides simple, safe syntax for performing complex data transformations in parallel and enables manipulation of native database data types at full precision. It also covers PROC FEDSQL, a modernized SQL language that blends perfectly with DS2. You will learn to harness the power of parallel processing to speed up CPU-intensive computing processes in Base SAS and how to achieve even more speed by processing DS2 programs on massively parallel database systems. Techniques for leveraging internet APIs to acquire data, avoiding large data movements when working with data from disparate sources, and leveraging DS2's new data types for full-precision numeric calculations are presented, with examples of why these techniques are essential for the modern data wrangler. Here's what's new in this edition: how to significantly improve performance by using the new SAS Viya architecture with its SAS Cloud Analytic Services (CAS) how to declare private variables and methods in a package the new PROC DSTODS2 the PCRXFIND and PCRXREPLACE packages While working though the code samples provided with this book, you will build a library of custom, reusable, and easily shareable DS2 program modules, execute parallelized DATA step programs to speed up a CPU-intensive process, and conduct advanced data transformations using hash objects and matrix math operations. This book is part of the SAS Press Series.
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Leverage Splunk's operational intelligence capabilities to unlock new hidden business insights and drive success About This Book Tackle any problems related to searching and analyzing your data with Splunk Get the latest information and business insights on Splunk 7.x Explore the all new machine learning toolkit in Splunk 7.x Who This Book Is For This book is intended for data professionals who are looking to leverage the Splunk Enterprise platform as a valuable operational intelligence tool. The recipes provided in this book will appeal to individuals from all facets of business, IT, security, product, marketing, and many more! Even the existing users of Splunk who want to upgrade and get up and running with Splunk 7.x will find this book to be of great value. What You Will Learn Learn how to use Splunk to gather, analyze, and report on data Create dashboards and visualizations that make data meaningful Build an intelligent application with extensive functionalities Enrich operational data with lookups and workflows Model and accelerate data and perform pivot-based reporting Apply ML algorithms for forecasting and anomaly detection Summarize data for long term trending, reporting, and analysis Integrate advanced JavaScript charts and leverage Splunk's API In Detail Splunk makes it easy for you to take control of your data, and with Splunk Operational Cookbook, you can be confident that you are taking advantage of the Big Data revolution and driving your business with the cutting edge of operational intelligence and business analytics. With more than 70 recipes that demonstrate all of Splunk's features, not only will you find quick solutions to common problems, but you'll also learn a wide range of strategies and uncover new ideas that will make you rethink what operational intelligence means to you and your organization. You'll discover recipes on data processing, searching and reporting, dashboards, and visualizations to make data shareable, communicable, and most importantly meaningful. You'll also find step-by-step demonstrations that walk you through building an operational intelligence application containing vital features essential to understanding data and to help you successfully integrate a data-driven way of thinking in your organization. Throughout the book, you'll dive deeper into Splunk, explore data models and pivots to extend your intelligence capabilities, and perform advanced searching with machine learning to explore your da...
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Over the past few years big data has emerged a new major pluri-disciplinary research area whose objective is to deal with massive datasets and that are too large to be manipulated by conventional modelling and current computational approaches. As the datasets often available in many disciplines and application areas exponentially grow thanks to the increasing availability of novel sensors and devices, there is nowadays an urgent need to develop novel methods and computational approaches to deal with the high volumes and variety of the large data sources generated in many fields. The range of research challenges still opened are extremely large, from the integration, storage, manipulation, data mining and visualization techniques to the development of computing architectures, cloud and distributed computing platforms to scalable storage systems. Clearly, big data challenges should require multidisciplinary approaches, when different points of view, ideas and experiences should be shared, this favoring exchanges and cross-fertilization on recent trends as well as research directions to consider.
Big data --- Data mining
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Big data --- Data sets, Large --- Large data sets --- Data sets
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Big data --- Data sets, Large --- Large data sets --- Data sets
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Big data --- Data sets, Large --- Large data sets --- Data sets
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