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1. BTM 382 Database Management
Chapter 2: Data models
Chapter 12-12: CAP
Chapter 14-2a: Hadoop
Chitu Okoli
Associate Professor in Business Technology Management
John Molson School of Business, Concordia University, Montréal

2. Structure of BTM 382 Database Management
 Week 1: Introduction and overview
 ch1: Introduction
 Weeks 2-6: Database design
 ch3: Relational model
 ch4: ER modeling
 ch6: Normalization
 ERD modeling exercise
 ch5: Advanced data modeling
 Week 7: Midterm exam
 Weeks 8-10: Database programming
 ch7: Intro to SQL
 ch8: Advanced SQL
 SQL exercises
 Weeks 11-13: Database management
 ch2,12,14: Data models
 ch13: Business intelligence and data warehousing
 ch9,14,15: Selected managerial topics

3. Review of Chapters 2, 12, 14:
Data models
 What is a data model?
 How have data models developed over the
years?
 What is the Object-Oriented Data Model
(OODM), and when is it useful?
 What is Big Data, and how does NoSQL
resolve the major Big Data challenges?
 Which data models should we use for which
situations?

4. Models and data models

5. What is a model?
 A model is a simplified way to describe or explain a
complex reality
 A model helps people communicate and work simply
yet effectively when talking about and manipulating
complex real-world phenomena

6. Scientific models
Image sources:
http://www.redorbit.com/education/reference_library/space_1/universe/2574692/geocentric_model/
http://hendrianusthe.wordpress.com/2012/06/21/heliocentric-vs-geocentric/

7. Conceptual models
Image sources:
http://info563.malagaclasses.info/strategy-it-2/
http://fivewhys.wordpress.com/2012/05/22/business-model-innovation/

8. Importance of Data Models
Communication tool
Give an overall view of the database
Organize data for various users
Are an abstraction for the creation of well-
designed good database

9. The Evolution of Data Models

10. Obsolete models:
Hierarchical and network models

11. The Relational Model
 Uses key concepts from mathematical relations (tables)
 “Relational” in “relational model” means “tables” (mathematical
relations), not “relationships”
 Table (relations)
 Intersections of
 rows (various data types) and
 columns (same data type)
 Relations have well defined methods (queries) for combining
their data members
 Selecting (reading) and joining (combining) data is defined based
on mathematical principles
 Relational data management system (RDBMS)
 Relations were originally too advanced for 1970s computing power
 As computing power increased, simplicity of the model prevailed

12. The Entity Relationship Model
 Enhancement of the relational model
 Relations (tables) become entities
 Very detailed specification of relationships and their properties
 Entity relationship diagram (ERD)
 Uses graphic representations to model database components
 Many variations for notation exist
 In this class, we use the Crow’s Foot notation

13. The Object-Oriented Data Model
(OODM)

14. The Object-Oriented Data Model (OODM)
 Tries to reconcile the ER model with
object-oriented programming (OOP)
 The ER model’s view of data (tables) and programmers’ view of data (objects
in OOP), is completely different
 This mismatch can sometimes make database programming painful,
especially for very complex data structures
 An OODM uses OOP concepts to store data
 Objects represent nouns (entities or records)
 Objects have attributes (properties or fields) with values (data)
 Objects have methods (operations or functions)
 Classes group similar objects using a hierarchy and inheritance
 In an OODBMS, the data retrieval and storage closely mirrors the data
structures that programmers use, and so programming complex objects
is much easier than with the ER model
 More advanced forms support the Extended Relational Data Model,
Object/Relational DBMS, and XML data structures

15. OODBMS vs. RDBMS
https://youtu.be/kORTgvfHl4g

18. Big Data and NoSQL

19. Explaining Big Data
https://youtu.be/7D1CQ_LOizA

20. Big Data
 Volume
 Huge amounts of data (terabytes and petabytes),
especially from the Internet
 Velocity
 Organizations need to process the huge amounts of
data rapidly, just as fast as with smaller databases
 Variety
 Many different types of data, much of it unstructured
and even changing in structure

21. How do you handle Big Data?
Where RDBMSs run into trouble
1. Solution: Scale up
 Use more powerful, expensive servers
 But RDBMSs are very computing intensive
 Big data would require much faster, more capable,
more expensive computers, and even that’s not good
enough for big data
2. Solution: Scale out
 Use many cheap distributed servers
 But RDBMS is slow with distributed processing
 Consistency is the biggest problem: guaranteeing
consistency (which RDBMS is great at) is slow
 Slow infrastructure isn’t good enough for big data

22. What is NoSQL?
https://youtu.be/qUV2j3XBRHc

23. NoSQL databases to the Big Data rescue
 “NoSQL” means:
 Non-relational or non-RDBMS
 Also “Not only SQL”—a few in fact do support SQL
 It is not one model; it is many different models that are not
relational data models
 Scale out (many cheap distributed servers) instead of scale up
 High scalability
 Support distributed database architectures
 High availability
 Rapid performance for big data, including unstructured and sparse data
 Fault tolerance
 Continue to work even if some servers in the cluster fail
 Emphasis is high performance speed, rather than
transaction consistency

24. Types of NoSQL databases
Image sources:
https://www.linkedin.com/pulse/20140823125259-38485481-nosql-databases-where-i-can-use?trk=sushi_topic_posts
http://www.monitis.com/blog/2011/05/22/picking-the-right-nosql-database-tool/
Also see:
Picking the Right
NoSQL Database Tool

25. Disadvantages of NoSQL
 Complex programming is often required
 “NoSQL” means you lose the ease-of-use and structural
independence of SQL
 There is often no built-in implementation of relationships in
the database—you might have to program relationships
yourself in code
 Data might be sometimes inconsistent
 No guarantee of transaction integrity
 Entity integrity and referential integrity not guaranteed
 The data you retrieve at any given moment might be
inaccurate… but it will eventually become OK
 This is the price to pay for rapid performance in a distributed
database

26. The CAP theorem for distributed databases
 CAP stands for:
 Consistency: All nodes see the same data
 Availability: A request always gets a response (success or failure)
 Partition tolerance: Even if a node fails, the system can still
function
 A distributed database can guarantee only two of the three
CAP characteristics, not all three at the same time
 Over time, it will eventually provide all three,
but it cannot guarantee all three at the same time
 NoSQL databases are distributed, and so the CAP theorem
restricts them to providing BASE, not ACID
Image source: PRWEB

27. ACID versus BASE
 A relational database guarantees the ACID properties:
 Atomicity, Consistency, Isolation, Durability
 In short, a set of SQL statements (called a transaction) will
either completely work or completely fail—no half way success,
and the result will not corrupt the database
 A price to pay: results might be somewhat slow
 A NoSQL database does not guarantee ACID; it only
guarantees BASE properties:
 Basically Available, Soft-state, Eventual consistency
 In short, at any given moment, not everything might be
consistent, but the database will eventually get consistent
 In return, these imperfect results are delivered fast

30. Summary of data models

31. Distributed Database Spectrum
Table 12.8
Sacrifices availability to ensure
consistency and isolation

32. Historical outline of data models

33. Which data model should you use?
 Hierarchical or network models
 Obsolete—no one uses these any longer
 Entity-relationship model
 Almost always
 90% or more of professional database situations
 Object-oriented database
 When you have very complex data structures, you need rapid
performance, and it helps achieve organizational objectives
 Source: Barry & Associates, Inc
 When data structures are so complex that organizing data as tables
causes headaches in programming retrieval and storage
 NoSQL
 When you have vast amounts of unstructured data and you need
rapid performance
 When speed is more important than data consistency
Popularity ranking of DBMSs: http://db-engines.com/en/ranking

34. Summary of Chapters 2, 12, 14:
Data models
 A data model is an abstract way of thinking about how
data is organized
 Although the relational model has become the dominant
data model, it cannot solve all database challenges
 The Object-Oriented Data Model is useful for complex
data coupled with object-oriented programming
 Big Data is data with high volume, velocity and variety
 NoSQL generally handles big data better than relational
databases, but it sacrifices consistency for speed
 No single data model is the best for all situations, so we
should understand the pros and cons of each model

36. Sources
 Most of the slides are adapted from Database
Systems: Design, Implementation and
Management by Carlos Coronel and Steven Morris.
11th edition (2015) published by Cengage Learning.
ISBN 13: 978-1-285-19614-5
 Other sources are noted on the slides themselves