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.