Data modeling. Presentation by – Anupama Vudaru, Phani Kondapalli Content by – Prathibha Madineni, Subrahmanyam Kolluri October 2010.

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

Day 1

A. Data Modeling overviewB. Data Modeling development life cycleC. Components of Data ModelingD. Data Modeling notations and design standardsE. Case study – CDM overview

Day 2

A. Conceptual data modelB. Types of Data modelingC. Various tools availableD. Developing CDM using ErwinE. Case study – LDM overview

Day 3

A. Logical data modelB. Developing LDM using ErwinC. Meta Data preservation for Design ConsiderationsD. Dimensional Data ModelingE. Case study – PDM overview

Day 4

A. Physical data modelB. Logical Data Model vs Physical Data ModelC. Developing PDM using ErwinD. Advanced Features of Erwin

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

Day 1

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

Is this how

my business

looks technically?

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

Second Phase

• 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.

1. Life cycle

First 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.

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

Total efforts

Documentation

And Meta data

Integration

Industry knowledge

Business Re-quirem

ents

Database Team

Executives

and End

Users

Development Team

RequirementsCDMLDMPDMDB

C. Components of Data Modeling

CONCEPTUAL MODELING

Subject

Represents a grouping of related information for a single subjectEx: Account

Relationship

Relationship between subject areas are identified in terms of cardinality One to one Many to Many One to Many

LOGICAL MODELING PHYSICAL MODELING

Entity

Subjects are narrowed down to specific objectsEx: Savings Account

Attribute

Property or characteristic of an entityEx: Number, Customer ID, Name, etc.

Relationship

Relationships between entities, as per the cardinality and type is identified Zero, one or more, Exactly Identified or Non-Identified

Table

A set of data elements organized in columns and rows

Column

Set of data element used to store specific type of data / values

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 StandardsOverall Data Model Standardsa. Data Model Nameb. Data Model DefinitionEntities and their Relationshipsc. Entity Named. Entity Definitione. Supertype/subtype Entityf. Entity Attributiong. Entity Normalization/De-normalizationh. Entity Relationship - Relationship Name, Attributesi. Attribute Namej. Attribute Definitionk. Unique IdentifiersDatabase Relatedl. Naming Standardsm. Common Database Standards

Refer to: http://mike2.openmethodology.org/wiki/Data_Modelling_Standards_Deliverable_Template