Download Data Modeling and Erwin

Data modeling.
Presentation by – Anupama Vudaru, Phani Kondapalli
Content by – Prathibha Madineni, Subrahmanyam Kolluri
October 2010
Preface
• Agenda – Basics of Data Modeling, Insurance industry and Erwin
• Duration and timings – 4 days x 2 hrs
• Expectations – In-class, hands on and post session work
• Course contents – Divided into slides, videos and print outs
• Legends used –
• Post-session work – Attendees are expected to do hands-on home work assigned for the day
Contents
A. Data Modeling overview
B. Data Modeling development life cycle
Day 1
C. Components of Data Modeling
D. Data Modeling notations and design standards
E. Case study – CDM overview
A. Conceptual data model
B. Types of Data modeling
Day 2
C. Various tools available
D. Developing CDM using Erwin
E. Case study – LDM overview
A. Logical data model
B. Developing LDM using Erwin
Day 3
C. Meta Data preservation for Design Considerations
D. Dimensional Data Modeling
E. Case study – PDM overview
A. Physical data model
Day 4
B. Logical Data Model vs Physical Data Model
C. Developing PDM using Erwin
D. Advanced Features of Erwin
Day 1
A. Data Modeling overview
B. Data Modeling development life cycle
C. Components of Data Modeling
D. Data Modeling notations and design standards
E. Case study – CDM overview
A. Data Modeling overview
1. What is a Data Model?
• Data modeling is the process of describing
information structures and capturing
business rules in order to specify
information system requirements.
• A conceptual representation of data
structures (tables) required for a database
• A graphical representation of
―Nature of data
―Business rules governing the data
―How it will be organized in the database
with less complexity
• A data model represents a balance between
the specific needs of a particular RDBMS
implementation project, and the general
needs of the business area that requires it.
Mrs. Smith’s video library
A. Data Modeling overview
2. Need for developing a Data Model
• A new application for OLTP (Online
Transaction Processing), ODS (Operational
Data Store), data warehouse and data marts.
• Rewriting data models from existing systems
that may need to change reports.
• Incorrect data modeling in the existing
systems
• A data base that has no data models.
• Effective means to express and communicate
the business requirements.
• Johns Life Insurance (JFI) corporation* is
a prominent life insurance provider in
Dream Valley nation. We shall be using
the case study of its business wherever
possible in these sessions.
*A
fictitious life insurance corporation designed for
this training.
A. Data Modeling overview
3. Benefits of Data Model
• The terms used in the model are stated in the language
of the business, not that of the system development
organization .
• Acts as a single version of truth and as a reference to:
―The DBA team to setup database
―The App dev team for macro designs and
development
―As a means of communication in the team and with
end-users
• Provides a clear picture of business relationships seen
as entity relations or referential integrity constraints
• Provides a logical RDBMS-independent picture of the
database
• Can be used to produce an executive summary diagram
B. Data Modeling development life cycle
1. Life cycle
First
Phase
Second
Phase
• Gathering Business Requirements
• Data Modelers interact with business analysts to get the functional requirements and with
end users to find out the reporting needs.
• Conceptual Data Modeling(CDM)
• CDM includes all major subjects and their components and inter dependencies.
• CDM contains business processes and regarding functioning of the organization.
Third
Phase
• Logical Data Modeling(LDM)
• LDM is the version of the model that represents entities, attributes and entity relationships.
• LDM can be validated against all of the business requirements of an organization.
Fourth
Phase
• Physical Data Modeling(PDM)
• PDM includes all required tables, columns, relationship, database properties for the physical
implementation of the database. PDM can be validated against the data flow.
Fifth
Phase
• Database creation
• DBAs instruct the data modeling tool to create SQL code from physical data model. Then the
SQL code is executed in server to create databases.
B. Data Modeling development life cycle
2. Process, efforts and timelines
•
•
•
•
•
•
•
Industry knowledge
Understand the business
requirements
Meets with business executives
Meets with end users
Meets development team
Meets database team
Documentation and Meta Data
Integration
Requirements
CDM
LDM
PDM
DB
Total efforts
C. Components of Data Modeling
CONCEPTUAL MODELING
LOGICAL MODELING
PHYSICAL MODELING
Subject
Entity
Table
Represents a grouping of related
information for a single subject
Subjects are narrowed down to
specific objects
A set of data elements organized
in columns and rows
Ex: Account
Ex: Savings Account
Column
Relationship
Attribute
Relationship between subject
areas are identified in terms of
cardinality
Property or characteristic of an
entity
Set of data element used to
store specific type of data /
values
 One to one
Ex: Number, Customer ID,
Name, etc.
 Many to Many
Relationship
 One to Many
Relationships between entities,
as per the cardinality and type is
identified
 Zero, one or more, Exactly
 Identified or Non-Identified
Constraint
Defined with relationship using
foreign keys constraints
Others
Column properties – data type,
length/precision/scale, null, key
type
Indexes – type and columns
Partitions – type and basis
D. Data Modeling notations and design standards
1. Notations
D. Data Modeling notations and design standards
2. Design Standards
Overall Data Model Standards
a. Data Model Name
b. Data Model Definition
Entities and their Relationships
a. Entity Name
b. Entity Definition
c. Supertype/subtype Entity
d. Entity Attribution
e. Entity Normalization/De-normalization
f. Entity Relationship - Relationship Name,
Attributes
a. Attribute Name
b. Attribute Definition
c. Unique Identifiers
Database Related
a. Naming Standards
b. Common Database Standards
Refer to:
http://mike2.openmethodology.org/wiki/Data_Modelling_Standards_D
eliverable_Template