Data Modelling
Prepared by Mohamed Osama Mortada
Copyright © Qeema 2023. All Rights Reserved
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Agenda
1. Data Modeling Definition
2. Data Modeling Activities
3. Data Modeling Schemes
Definition
• Data Modeling:
The process of discovering, analyzing, and scoping data requirements, and then representing and
communicating these requirements in the form of a data model. The data model forms are logical and
physical.
• Goals:
1. Formalization
2. Scope definition
3. Knowledge retention/documentation
Activities &Lifecycle
Inputs
Activities
Key Players
Tools & Techniques
Output
Data Modeling
Schemes
• Relational
• Dimensional
• Time-Based
• NoSQL
Relational Models ERD
Components
• Entity
• Relationships
• Attributes
Relational Models
Relationship Type
Components
• Entity
• Relationships
• Attributes
The number of entities in a relationship is the ‘arity’
of the relationship. The most
common are unary, binary, and ternary
relationships.
Relational Models
Relationship Cardinality
Components
• Entity
• Relationships
• Attributes
captures how many of one entity (entity instances)
participates in the relationship with how many of
the other entity
Relational Models
Participation Constraint
Components
• Entity
• Relationships
• Attributes
captures the minimum existence of an entity in the
relationship (Partial/Optional or Total/Must)
ERD Components
Components
• Entity
• Relationships
• Attributes
Example
Test yourself
UPS prides itself on having up-to-date information on the processing and current location of each shipped item.
To do this, UPS relies on a company-wide information system.
Shipped items are the heart of the UPS product tracking information system.
Shipped items can be characterized by item number (unique), weight, dimensions, insurance amount,
destination, and final delivery date.
Shipped items are received into the UPS system at a single retail center.
Retail centers are characterized by their type, uniqueID, and address.
Shipped items make their way to their destination via one or more standard UPS transportation events (i.e.,
flights, truck deliveries). These transportation events are characterized by a unique scheduleNumber, a type (e.g,
flight, truck), and a deliveryRoute.
Please create an Entity Relationship diagram that captures this information about the UPS system. Be certain to
indicate identifiers and cardinality constraints.
Normalization vs
Denormalization
Normalization
is the method of arranging the data in the database efficiently. It involves constructing tables and setting
up relationships between those tables according to some certain rules. The redundancy and inconsistent
dependency can be removed using these rules in order to make it more flexible.
Normalization have the following forms: 1NF, 2NF, 3NF …
The purpose of Normalization is to eliminate redundant (useless) data and ensure data is stored logically.
Denormalization
is the inverse process of normalization where the redundancy is added to the data intentionally to improve
the performance of the specific application and data integrity. The reason for performing denormalization
is the overheads produced in the query processor by an over-normalized structure. Denormalization
reduces the number of tables, and the complicated table joins because a higher number of joins can slow
down the process.
Normalization –
Deep dive
Why do we normalize?
• Save space
• Eliminate Update anomalies
• Insertion anomalies
•
Cannot insert a project unless an employee is assigned to it
• Delete anomalies
•
When a project is deleted all employees working on that project will be deleted as well
• Modification anomalies
•
Changing the name of a project from “Billing” to “Customer Invoicing” will cause update for 100
rows in the dataset.
Normalization –
Deep dive
Normalization –
Deep dive
Normal Form:
are a series of guidelines that help to ensure that the design of a database is efficient, organized, and free
from data anomalies. There are several levels of normalization, each with its own set of guidelines, known as
normal forms.
The guidelines for each normal form is as follows:
• 1NF: Eliminate Repeating Groups & MV Attributes
• 2NF: Eliminate Partial FD
• 3NF: Eliminate Transitive Dependency
Normalization – 1NF
Normalization – 1NF
Normalization – 2NF
Normalization – 3NF
Mapping ER diagram
to Relational Schema
Methodology:
• Step 1: Mapping of Regular Entity Types
• Step 2: Mapping of Weak Entity Types
• Step 3: Mapping of 1:1 Relation Types
• Step 4: Mapping of 1:N Relation Types
• Step 5: Mapping of M:N Relation Types
• Step 6: Mapping of Multivalued attributes
Example
Mapping ER diagram
to Relational Schema
Normalization –
Deep dive
Questions ?
Email:
mohamed.mortada@qeema.net