1. Chapter 2
Data Models
Database Systems: Design, Implementation, and Management, Eleventh Edition, Coronel & Morris

2. Data Models Big Concept definition Small Concept definition
School of thought as to what and how a database should work and the technologies that it should be based on.
Small Concept definition
Representation, usually graphical, of the structure of a database solution for a given business problem.
Both views of a Data Model are very important!
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

3. The Importance of Data Models (Small Concept definition)
Relatively simple representations, usually graphical, of complex real-world data structures
Facilitate interaction among the designer, the applications programmer, and the end user
End-users have different views and needs for data
Data model organizes data for various users
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

4. Data Model Basic Building Blocks (Small Concept definition)
Entity - anything about which data are to be collected and stored
Attribute - a characteristic of an entity
Relationship - describes an association among entities
One-to-many (1:M) relationship
Many-to-many (M:N or M:M) relationship
One-to-one (1:1) relationship
Constraint - a restriction placed on the data
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

5. Business Rules
Brief, precise, and unambiguous descriptions of a policies, procedures, or principles within a specific organization
Description of operations that help to create and enforce actions within that organization’s environment
Sources of Business Rules:
Company managers
Policy makers
Department managers
Written documentation
Procedures
Standards
Operations manuals
Direct interviews with end users
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

6. The Evolution of Data Models (Big Concept defintion)
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

7. The Relational Model Developed by E.F. “Ted” Codd (IBM) in 1970
Considered ingenious but impractical
Conceptually simple
Computers lacked power to implement the relational model
Today, microcomputers can run sophisticated relational database software
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

8. The Relational Model (continued)
Relational Database Management System (RDBMS)
Most important advantage of the RDBMS is its ability to hide the complexities of the relational model from the user
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

9. The Relational Model (continued)
Table (relations)
Matrix consisting of a series of row/column intersections
Related to each other through sharing a common entity characteristic
Relational table is purely logical structure
How data are physically stored in the database is of no concern to the user or the designer
This property became the source of a real database revolution
Relational diagram
Representation of relational database’s entities, attributes within those entities, and relationships between those entities
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

10. The Relational Model (continued)
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

11. The Relational Model (continued)
Rise to dominance due in part to its powerful and flexible query language
Structured Query Language (SQL) allows the user to specify what must be done without specifying how it must be done
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

12. The Entity Relationship Model
Widely accepted and adapted graphical tool for data modeling
Introduced by Peter Chen in 1976
Graphical representation of entities and their relationships in a database structure
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

13. The Entity Relationship Model (continued)
Entity relationship diagram (ERD)
Uses graphic representations to model database components
Entity is mapped to a relational table
Entity instance (or occurrence) is a row in a table
Entity set is collection of like entities
Connectivity labels types of relationships
Diamond connected to related entities through a relationship line
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

14. The Entity Relationship Model
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

15. Comparison of Representations
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris

16. Big Data Exponential growth in data Business data
2005, largest data warehouse was 100TB
2013, eBay “singularity system” was 40,000TB
Consumer-generated data
Twitter generates over 8TB of data every day
Machine-generated data
Boeing engine generates 10TB every 30 minutes
(over 25,000 airline flights/day = a total of more than 1000TB /day)
Large Hadron Collider generates 40TB every second

17. Why is Big Data Different
Volume
Variety
Velocity
Data Products – the data is the product
Google
Amazon

18. Conceptual Alternatives (NoSQL)
Key-Value Store
Document Database

19. Map Representations
{“1” : {“Title”: “How to X”,“Author”: “Jane Doe”,“Categories”: {“foo” : “”,“bar” : “”},“Content”: “So you want to X? Here’s…”}, “2” : {“Title”: “X Y About Z”,“Author”: “John Doe”,“Content”: “Here’s a list of Y about Z…”}, “3” : {“Title”: “Why X Instead of Z”,“Author”: “Jane Doe”,“Categories”: {“foo”: “”,},“Content”: “Some people think Z, but…”}}

20. MapReduce Combination of the Map and Reduce algorithms
Map – partition a task into several smaller subtasks, each assigned to a different worker process
Reduce – aggregate the results of the map workers into a single result set.
Written primarily in Java.
Use pre-defined queries.

21. Relational and NoSQL Differences
Structure requirements
RDBMS: Structured in storage
Big Data: Unstructured in storage, structure imposed during processing
Integrity constraints
RDBMS: Ensuring consistency and integrity requires overhead
Big Data: Data captured “as is”
Distributed limitations
RDBMS: ACID transactions require greater synchronization between distributed nodes
Big Data: Designed to be highly distributed over 1,000s of nodes without enforcement of strict consistency

22. Relational vs. NoSQL example
Word Count (count how many times each word appears in the data)
NoSQL partial solution (as provided in Hadoop Tutorial)
SQL solution
public static class MapClass extends MapReduceBase
implements Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output,
Reporter reporter) throws IOException {
String line = value.toString();
StringTokenizer itr = new StringTokenizer(line);
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
output.collect(word, one); }
public static class Reduce extends MapReduceBase
implements Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> values,
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
output.collect(key, new IntWritable(sum));
SELECT Word, Count(*)
FROM Res_Ad_Words
GROUP BY Word;
Database Systems: Design, Implementation, & Management, 11th Edition, Coronel & Morris