Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. Learn general methods for specifying Big Data in a way that is understandable to humans and to computers Avoid the pitfalls in Big Data design and analysis Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources
Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications.
The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.
... which is based in California, US From the International Technical Support Organization Mary Comianos: Publications Management, San Jose, CA Ann Lund: Residency Administration, Poughkeepsie, NY Wade Wallace: Editor, Austin, TX ...
Computational Science (CS) and Computational Intelligence (CI) both share the same objective: finding solutions to ... Material sciences; Space, weather, climate systems, and global changes; Computational ocean and earth sciences; ...
The reader follows one reference scenario through the whole book, that uses an open Apache dataset. The origins of this volume are in lectures from a master’s course in Data-intensive Systems, given at the University of Stavanger.
Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.
Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how ...
This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?
This book examines ethical questions raised by the big data phenomenon, and explains why enterprises need to reconsider business decisions concerning privacy and identity.