Ricardo Catherine M. - Databases Illuminated

DATABASES ILLUMINATED

THIRD EDITION

Catherine M. Ricardo

Iona College

Susan D. Urban

Arizona State University

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Library of Congress Cataloging-in-Publication Data

Ricardo, Catherine M.

Databases illuminated.Third edition / Catherine M. Ricardo, Iona College, New Rochelle, New York, Susan D. Urban, Arizona State University, Tempe, Arizona.

pages cm

Includes bibliographical references and index.

ISBN 978-1-284-05694-5 (casebound)

1. Database management. I. Urban, Susan D. II. Title.

QA76.9.D3R514 2017

005.74dc23

2015008322

6048

Printed in the United States of America

19 18 17 16 15 10 9 8 7 6 5 4 3 2 1

For Catherine M. Ricardo

To my husband, Henry J. Ricardo, my children Henry, Jr., Cathy, and Christine, and my grandchildren, Tomas, Nicholas, Christopher, Joshua, and Elizabeth.

For Susan D. Urban

To my husband, Joseph E. Urban. Thanks for all of your love and support.

Table of Contents

Preface

Purpose of This Book

Databases have become a central element of the framework of modern computer systems. So it follows that the study of database systems, design, and management is an essential part of the education of computer science and information science majors. A course in database technology should provide students with a strong theoretical background, practice in database design, and experience creating and developing a working database. Students should also be exposed to emerging data management issues and new directions for database technology. This book is designed to help students integrate theoretical material with practical knowledge, using an approach that applies theory to practical database implementation. This book will also help students understand the difference between traditional database technology and the new directions created by big data, outlining the motivation, fundamental concepts, technologies, and challenges associated with handling large datasets.

Structure

Theoretical foundations are presented early, and the concepts reappear throughout the book, including in the chapters that address implementation. Logical database design is given full consideration. The entity-relationship (E-R) model is introduced early and then mapped to the relational model. Relational normalization is studied in detail, and many examples of the normalization process are discussed. The object-oriented model is presented using Unified Modeling Language (UML) as a vehicle for logical design. The enhanced entity-relationship (EE-R) model is presented and then mapped to both relational and object-relational models. Extensible Markup Language (XML) and the semi-structured data model are also introduced.

A continuing example of a university database is incorporated throughout the text, to illustrate concepts and techniques and to provide both continuity and contrast. Other examples are presented as needed. Purely relational, object-relational, and object-oriented database systems are described and used for implementation of the examples. Details of database management systems are explored so that students can learn the specifics of these real-life systems down to the level of physical implementation. LibreOffice Base is used initially in the examples, but Oracle is introduced as the material is developed. The examples are suitable, however, for use with any relational or object-relational database management system (DBMS). The InterSystems Cach DBMS is used to illustrate object-oriented databases.

The fundamentals of storing and accessing large data sets are introduced using Hadoop and its two main components: the Hadoop distributed file system and the MapReduce parallel programming paradigm. The Hive data warehouse infrastructure of the Hadoop environment is examined as a means for expressing Structured Query Language (SQL)-like queries over large files. HBase is also examined as a column-oriented approach to the scalable, distributed storage of large tables. The IBM BigInsights Quick Start Edition Console is used for all examples of big data in the book. NoSQL and NewSQL are also addressed as variations of big data technology.

New to This Edition

New chapter on the emerging topics of big data, NoSQL, and NewSQL

Coverage of Apache Hadoop, Hive, and HBase, with accessible examples of each