Автор: Dan Linstedt Название: Building a Scalable Data Warehouse with Data Vault 2.0 ISBN: 0128025107 ISBN-13(EAN): 9780128025109 Издательство: Elsevier Science Рейтинг: Цена: 9262.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
TheData Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures.
"Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss:
How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes.
Important data warehouse technologies and practices.
Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture.
Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast
Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse
Demystifies data vault modeling with beginning, intermediate, and advanced techniques
Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0
Автор: Foster Provost Название: Data Science For Business: What You Need To Know About Data Mining And Dataanalytic Thinking ISBN: 1449361323 ISBN-13(EAN): 9781449361327 Издательство: Wiley Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть (1 шт.) Описание: This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect.
Автор: Brown Meta S. Название: Data Mining for Dummies ISBN: 1118893174 ISBN-13(EAN): 9781118893173 Издательство: Wiley Рейтинг: Цена: 5067.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum.
Автор: Hamstra Mark, Zaharia Matei Название: Learning Spark: Lightning-Fast Big Data Analytics ISBN: 1449358624 ISBN-13(EAN): 9781449358624 Издательство: Wiley Рейтинг: Цена: 5067.00 р. Наличие на складе: Поставка под заказ.
Описание: Written by the developers of Spark, this book will have data scientists and engineers up and running in no time.
Автор: Wheelan Charles Название: Naked Statistics: Stripping the Dread from the Data ISBN: 039334777X ISBN-13(EAN): 9780393347777 Издательство: Wiley Рейтинг: Цена: 2216.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A New York Times bestseller "Brilliant, funny...the best math teacher you never had." -San Francisco Chronicle
Автор: Jiawei Han Название: Data Mining: Concepts and Techniques, ISBN: 0123814790 ISBN-13(EAN): 9780123814791 Издательство: Elsevier Science Рейтинг: Цена: 9720.00 р. Наличие на складе: Поставка под заказ.
Описание: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.
Автор: Lior Rokach Название: Data Mining with Decision Trees ISBN: 981459007X ISBN-13(EAN): 9789814590075 Издательство: World Scientific Publishing Цена: 16632.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.
This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.
This book invites readers to explore the many benefits in data mining that decision trees offer:
Self-explanatory and easy to follow when compacted
Able to handle a variety of input data: nominal, numeric and textual
Scales well to big data
Able to process datasets that may have errors or missing values
High predictive performance for a relatively small computational effort
Available in many open source data mining packages over a variety of platforms
Useful for various tasks, such as classification, regression, clustering and feature selection
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Описание: With contributions from more than two dozen experts, this book demonstrates why visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding.
Описание: This mini book will act as an extension to Programming Entity Framework 2nd Edition. Code First is an additional means of building a model to be used with the Entity Framework and is creating a lot of excitement in the .NET development community.
Автор: С.Aggarwal Название: Data Mining: The Textbook ISBN: 3319141414 ISBN-13(EAN): 9783319141411 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Поставка под заказ.
Описание: This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.
Learn how to take full advantage of Apache Kafka, the distributed, publish-subscribe queue for handling real-time data feeds. With this comprehensive book, you ll understand how Kafka works and how it s designed. Authors Neha Narkhede, Gwen Shapira, and Todd Palino show you how to deploy production Kafka clusters; secure, tune, and monitor them; write rock-solid applications that use Kafka; and build scalable stream-processing applications.Learn how Kafka compares to other queues, and where it fits in the big data ecosystemDive into Kafka s internal designPick up best practices for developing applications that use KafkaUnderstand the best way to deploy Kafka in production monitoring, tuning, and maintenance tasksLearn how to secure a Kafka clusterGet detailed use-cases"
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
