1. INFS 3220 Systems Analysis & Design
Transactional DBs vs. Data Warehouses

2. Relational Databases (RDBMS)
Collection of linked tables
Tables linked by Primary Key / Foreign Key relationships (Referential Integrity)
Primary Key – column whose values make each record unique
Foreign Key – value in column that links to Primary Key in another table
SQL – Structured Query Language (language to access data in relational tables)

3. Relational DB Example Cust # Cust Name 100 Bob 101 Sue 102 Juan
Order # Prod# Qty Cust#
1 QR
2 QR
3 SB
CUSTOMER TABLE
ORDER TABLE
Primary Key
Foreign Key

4. Database Structure & Design
2 Approaches:
Conflict
1. Optimize for
Data Capture
i.e., Capturing Transactions
2. Optimize for
Data Access
i.e., Queries & Reporting

5. Approach #1: Optimize for Data Capture
To optimize for data storage, you must:
Eliminate redundancy of data (or else wasted space & processing occurs)
Ensure data integrity (or else data anomalies)
Ensure that changes in data (modifications, deletions, etc. only have to happen in one place)
Normalization – process in which a DBMS is optimized for data storage
All data “redundancy” is removed from Database
Has multiple forms (0, 1st, 2nd, 3rd, et al.)

6. Moving from 0NF to 1NF Rule: Make a separate table for each set of related attributes, and give each table a primary key of unique values.
Cust # CustName
100, 100, 101 Bob, Sue, Juan
CUSTOMER TABLE
ONF
Cust # Cust Name
100 Bob
101 Sue
102 Juan
CUSTOMER TABLE
1NF
Primary Key Created with Unique values

7. Moving from 1NF to 2NF Rule: Eliminate any repeating values caused by a dependency on a “keyed” column (i.e., either Primary or Foreign)
Cust # Cust Name Order#
100 Bob 1
100 Bob 2
101 Sue 3
TABLE X
1NF
100 Bob
Dependency on Primary Key
Cust # Cust Name
100 Bob
101 Sue
Order # Cust#
1 100
2 100
3 101
CUSTOMER TABLE
ORDER TABLE
2NF

8. Moving from 2NF to 3NF Rule: Eliminate any repeating values caused by a dependency on a “non-keyed” column (i.e., dependency on ANY column)
Cust # City Order# ShipTime
100 PGH 1 2 days
101 PGH 2 2 days
102 LA 3 5 days
TABLE X
2NF
PGH 2 days
Dependency b/t 2 non-key columns
City # City ShipTime
10 PGH 2 days
20 LA 5 days
Cust # City#
100 10
101 10
102 20
SHIP TIME TABLE
CUSTOMER TABLE
3NF

9. Normalized DB Example

10. To optimize for data access, you must:
Approach #2: Optimize for Data Access (in a separate, read-only Data Warehouse)
To optimize for data access, you must:
Allow data redundancy
Reduce the number of table joins (links among tables)
Denormalizing – Adding redundancy & reducing joins in a DBMS

11. Denormalizing – Most Common Approach
Star Schema (Clustering)
Fact (core or transaction) Tables in middle of star
Dimensional (structural or “lookup”) Tables around “points” of star
Cust # CustName
100 Bob
101 Sue
102 Juan
Rep # RepName
1000 Lee
2000 James
3000 Natasha
REP TABLE
CUSTOMER TABLE
Order # Date Cust# Prod# Rep#
1 06/15/XX 100 QR
2 07/19/XX 100 QR
3 08/30/XX 101 SR
ORDER TABLE
Date Quarter
06/29/XX 2 Bob
06/30/XX 2 Sue
07/01/XX 3 Juan
Prod # ProdName
QR22 Rake
SR56 Spade
TW43 Mulch
PRODUCT TABLE
DATE/TIME

12. Denormalizing (continued) • Stars are linked via common (i. e
Denormalizing (continued) • Stars are linked via common (i.e., Conformed) Dimensions to form Data Warehouse
Cust # CustName
100 Bob
101 Sue
102 Juan
Rep # RepName
1000 Lee
2000 James
3000 Natasha
REP TABLE
CUSTOMER TABLE
Order # Date Cust# Prod# Rep#
1 06/15/XX 100 QR
2 07/19/XX 100 QR
3 08/30/XX 101 SR
ORDER TABLE
ORDER TABLE
Prod # ProdName
QR22 Rake
SR56 Spade
TW43 Mulch
PRODUCT TABLE
Date Quarter
06/29/XX 2 Bob
06/30/XX 2 Sue
07/01/XX 3 Juan
CUSTOMER TABLE
DATE/TIME
TIME
Prod# ProdName Stock Date Units
QR22 Rake 03/23/XX
TW43 Mulch 04/15/XX 1452
SR56 Spade 05/01/XX
INVENTORY TABLE