The book maintains a very practical approach to introducing the principles of database management for an undergraduate database management course.

The sequence of material is also sensible; it covers the most compelling topics: database modeling; relational, object-oriented, and NoSQL databases; distributed data management; and Extensible Markup Language (XML) interfaces, including some big data contexts.

There are many books on the market dealing with the subject, but this book can be used as the primary textbook for respective graduate courses. It is clearly the result of a series of courses the authors prepared during their tenure.

The opening chapters immediately illustrate the authors’ approach to mixing theoretical concepts and concrete industrial examples. Equipped with exercises (“Review Questions”) of varying complexity, the book allows readers to check their understanding. Within this context, the book succeeds in the balancing act of finding the right mixture of theoretical and practical knowledge.

One cannot miss a chapter on analytics. Unfortunately, the authors miss the opportunity to link the subject of analytics to many topics covered in the book. For instance, a few databases now have analytical capabilities built-in, including some that treat a machine learning model as a database entity. As a result, the chapter on analytics is somewhat of a dangling pointer. It is still useful because the reader is informed about the rapidly changing subject of the improving return on investment (ROI) of analytics, privacy, and security.

Overall, the book is a nice addition to extensive literature on database design principles, with insights into big data techniques, analytics, and data science.

Anyone teaching a course on database management will greatly appreciate it. The book will certainly appeal to a larger audience of postgraduate students and research engineers as well.

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