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Database Concepts
8th Edition
David M. Kroenke • David J. Auer • Scott L. Vandenberg • Robert C. Yoder
Instructor’s Manual
Prepared by Robert C. Yoder
APPENDIX K
BIG DATA
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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted,
in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior
written permission of the publisher. Printed in the United States of America.
Instructor’s Manual to accompany:
Database Concepts (8th Edition)
David M. Kroenke • David J. Auer • Scott L. Vandenberg • Robert C. Yoder
Database Concepts Big Data Appendix K
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APPENDIX OBJECTIVES
• Learn the basic concepts of Big Data
• Learn the basic concepts of non-relational database management systems
• Learn about replicated, partitioned data stores for supporting clusters
• Understand the limitations and trade-offs of replicated, partitioned stores as indicated
by the CAP theorem
• Learn the basic concepts of key-value databases
• Learn the basic concepts of document databases
• Learn the basic concepts of column family databases
• Learn the basic concepts of graph databases
• Understand the importance of XML
• Learn the elements of XML documents
• Understand how to describe and validate XML document structure using XML
Schema
• Understand the role of XSLT in materializing XML documents
• Learn the basic concepts of JSON as a way of structuring nonrelational data
• Obtain a practical introduction to the Microsoft Azure cloud environment to set up an
account, create SQL and NoSQL DocumentDB databases, and run simple queries.
CHAPTER ERRATA
There are no known errors at this time. Any errors that are discovered in the future will
be reported and corrected in the Online DBC e08 Errata document, which will be
available at http://www.pearsonhighered.com/kroenke .
THE ACCESS WORKBENCH
There is no section of The Access Workbench associated with this appendix.
NOTES ON MICROSOFT WINDOWS 10
This book uses the Microsoft Windows 10 operating system as the basis for screenshots
and step-by-step instructions. However, with Windows 10, Microsoft has introduced a
continuous update system that has already resulted in some fundamental differences in
how different versions of Windows 10 look and operate.
Database Concepts Big Data Appendix K
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For example, in the original version of Microsoft Windows 10, clicking the Windows
Start button (or pressing the Windows key on the keyboard) displayed the menu shown
in Figure 1. In this menu, we need to click the All apps button in order to see the All
apps menu shown in Figure 2.
Figure 1 – Windows 10 Main Menu
The All apps button
The File Explorer button
The File Explorer icon
Database Concepts Big Data Appendix K
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Figure 2 – Windows 10 All Apps Menu
Figure 3 – Windows 10 Anniversary Update Main Menu with All Apps Menu Included
The All apps menu
The All apps menu
The File Explorer button
The File Explorer icon
Database Concepts Big Data Appendix K
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Microsoft then released the Windows 10 Anniversary Update (Feature update to
Windows 10, version 1607) (see the blog discussion at
https://blogs.windows.com/windowsexperience/2016/08/02/how-to-get-the-windows-10-
anniversary-update/#K1CZuiw4auiuE9A5.97 ). One of the changes introduced in the
Anniversary Update was a major change to the menu system. Now, as shown in
Figure 3, the All apps menu is immediately available when the Start button is used (or
the keyboard Windows key is pressed).
Therefore, note that the step by step instructions in this book may need to be altered for
your use depending upon which version of Microsoft Windows 10 you or your students
are using!
We recommend that you update Windows 10 to the Windows 10 Anniversary Update
(Feature update to Windows 10, version 1607), and make sure it is patched with all
updates to that version (at a minimum patched to Windows 10 Version 1607 update for
August 23, 2016 (KB3176936), and the Windows 10 Version 1607 cumulative update for
September 29, 2016 (KB3194496). We also recommend using the 32-bit version of
Microsoft Office. This insures that all the examples discussed in this book will function
properly.
TEACHING SUGGESTIONS
• Have a class discussion with students about Big Data and its relationship to Cloud
Computing. Due to the needs of large applications like Facebook and other social
media to accommodate the Three V’s (velocity, variety and volume), traditional
relational databases can be too slow or are difficult to scale up to meet storage and
processing needs. Thus, new database technologies have been developed for “the
NoSQL movement.” Relational database joins can be slow, and the unstructured
nature of some types of data make schemas and traditional relations unwieldy.
• Explain to your students that big data systems already have an important role in
business operations, and the importance of this role should only increase over time.
If you know of any local examples, use them to illustrate your point.
• Ask the students to think about ways in which big data can be made more useful to
decision makers. How can data be made more relevant?
• Explain to your students that cloud computing providers have massive data centers
that can scale up storage and processing to meet the needs of big data. Thus,
“renting” computing power allows companies to pay for only what they use, avoiding
a large capital cost outlay for computers, storage, networking equipment, air
conditioning, software licenses and data center personnel.
• This appendix introduces some advanced topics of database processing used in
big data systems. It is intended to supplement Chapter 8 in the book. Each of these
topics is only briefly touched upon in this chapter. There is more information on each
of them in David M. Kroenke and David J. Auer, Database Processing:
Fundamentals, Design, and Implementation (14th Edition), Upper Saddle River:
Prentice Hall (2016).
Database Concepts Big Data Appendix K
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ANSWERS TO REVIEW QUESTIONS
K.1 What is the NoSQL movement?
K.2 What is the difference between sharding and replication in a distributed data store?
K.3 What is a replica set, and what does it have to do with eventual consistency?
K.4 What are the original three Vs? Define each term.
K.5 What is a quorum, and what is its purpose for a distributed data store?
K.6 What are the four categories of NoSQL databases used in this book?
K.7 What is the CAP theorem? How has it stood up over time?
Database Concepts Big Data Appendix K
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K.8 Why do database processing and document processing need each other?
K.9 How are HTML, SGML, and XML related?
K.10 Explain the phrase “standardized but customizable.”
K.11 What is SOAP? What did it stand for originally? What does it stand for today?
K.12 What are the problems in interpreting a tag such as <H2> in HTML?
K.13 What is an aggregate? What are some of the NoSQL databases that support
aggregates?
K.14 Why is it too limiting to say that XML is just the next version of HTML?
Database Concepts Big Data Appendix K
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K.15 How are XML, XSL, and XSLT related?
K.16 Explain the use of the pattern {item, action} in the processing of an XSL document.
K.17 What is the purpose of XML Schema?
K.18 What is the relationship between aggregates and XML or JSON?
K.19 What is a schema-valid document?
K.20 Explain the chicken-and-egg problem concerning the validation of XML Schema
documents.
K.21 Explain the difference between simple and complex elements.
K.22 Explain the difference between elements and attributes.
K.23 Explain, in your own words, why XML is important to database processing.
K.24 Why is XML Schema important for interorganizational document sharing?
Database Concepts Big Data Appendix K
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K.25 What was the first nonrelational data store to be developed, and who developed it?
K.26 As illustrated in Figure K-8, what is column family database storage and how are such
systems organized? How do column family database storage systems compare to
RDBMS systems?
Figure K-8 is shown on the next page.
Database Concepts Big Data Appendix K
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K.27 What is graph database? What are nodes, properties and edges?
Based on mathematical graph theory, graph databases are composed of three elements:
Database Concepts Big Data Appendix K
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K.28 What is a key value database? Under what circumstances is one most useful? Where
does processing relating to the structure of data values take place?
K.29 What are the main features of data in a document database? What are the basic
operations and utilities provided by a document DBMS?
K.30 Develop an XML Schema for a document that uses the data in the HSD CUSTOMER
table. Create an example of an XML document for your schema that represents the first
two customers (see Figure 3-38 for HSD data). If you need more information about XML
Schema, go to W3C and read tutorials on XML and XML Schema.
A note on XML Schema cardinality and data types:
XML and XML Schema are very complex topics. For more detailed information, see
http://www.w3.org/XML/ and http://www.w3.org/XML/Schema .Two topics that need more
Database Concepts Big Data Appendix K
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Data Type:
XML Schema property:
Boolean
type="xsl:boolean"
Character
type="xsl:string"
Date
type="xsl:date"
Date Time
type="xsl:date Time" [the spelling is correct]
Decimal
type="xsl:decimal"
Floating Point
type="xsl:float"
Int
type="xsl:int"
Integer
type="xsl:integer"
Time
type="xsl:time"
There is not a Money data type in XML Schema — use Decimal instead.
A note on file locations:
All files for this set of questions were located on a workstation with the Microsoft IIS Web server
installed and in a workstation directory named:
Database Concepts Big Data Appendix K
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The XML Customer.xsd schema definition file is:
<?xml version="1.0" encoding="UTF-8"?>
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema">
<xsd:element name="CustomerList">
<xsd:annotation>
Our Customer.xml (data file) is:
<?xml version="1.0" encoding="UTF-8"?>
<CustomerList>
<Customer>
<CustomerID>1</CustomerID>
<LastName>Jacobs</LastName>
Database Concepts Big Data Appendix K
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K.31 Develop a partial graph database based on a portion of the WP EMPLOYEE and
PROJECT tables, that will look similar to Figure K-9. Use WP data shown in Figure 3-2
to model only Project 1300 and the employees assigned to project 1300. The Project
and the Employees will be nodes, the Assignment relationship is bidirectional
(AssignedTo and ProjectWorkers) and becomes the edges and labels; properties will be
from the data fields.
The graph database shows only part of the data in the relational database, but included as
sample data all EMPLOYEES assigned to PROJECT 1300 - 2017 Q3 Tax Preparation.
Database Concepts Big Data Appendix K
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K.32 Using the JSON examples in Appendix K, develop a JSON file describing the first
customer from the HSD CUSTOMER table described in Figures 3-37 and 3-38. Be sure
to represent CustomerID (value will be 1). Use the expressive power to JSON to extend
the data to allow multiple phone numbers, and also to express Address as a composite
containing StreetAddress, City, State, and ZIP. Make up a second phone number for
Nancy Jacobs and enter it.
{
CustomerID: 1,
LastName: "Jacobs",
FirstName: "Nancy",
}
