01-data modeling intro_v2

8/7/2019 01-Data Modeling Intro_v2

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Data Modeling Overview

DB 210 Data Modeling


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Modeling


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Discussion Mind Map of Model

Easy to

use

Can

occupy

less space

Can be a

reasonable

substitute

Is a poor

substitute

Can be

thrown

away

Less

expensiv

e

Not real

Professional

handling not

required


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Discussion Examples

Examples of real modelsAircraft model

Building model

Fashion models too! Examples of abstract models

Financial Models

Data ModelsSimulation Models


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Discussion Why models?

Real entities are not available Dinosaurs!

Real entities are expensive tobuild Aircrafts

Real entities are voluminous Databases

Real entities do not even exist Financial Models

There are many reasons why we need models


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Models in Software

Analysis ModelsExample: Data Flow Diagrams

Design Models

Example: Structure Charts, StateDiagrams

Execution Models

Call graphs, coverage analysis,performance charts

Infrastructure Models

Example: Network diagrams


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Modeling Summary

Modeling is the process ofabstracting reality

Modeling is imperative to tackle large

problems as the real problem may betoo large to comprehend in itsentirety

Visual Modeling is the processabstracting reality in the form ofpictures


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Data Modeling


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Data Modeling Example

2574

John Doe

A17

7

36

Bangalore

Chennai

Software Engineer

AsiaPac

janedoe

He11o!

2574

Dane Doe

423

A12

Bangalore

Chennai

EMEA jdoe

we1c0me!


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Data Modeling Example

2574 John Doe 3536 Cochin A12 Asia jdoe He11o!

ID Name OfficeAge Home Grade Domain Userid Password

1364 Dane Doe 2142 Chennai A17 EMEA ddoe We1c0me!

EMPLOYEE

This way ofabstracting data using

meaningful concepts from the domain

and structuring them using specificnotations is called data modeling.


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Key Data Modeling Concepts

Data Model

Abstraction

Schema

Database

Design

Database

Mapping


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Data Model is acollection of concepts(notations,

terminology, etc.) thathelp model dataData Model


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The key idea behinddata modeling is toavoid dealing withdata directly as muchas possible

This is achieved bychoosing relevant

terminology andnotation dependingupon the data model

Abstraction


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Structural descriptionof data created usingthe notations andconcepts defined in aparticular data modelExample: A relational

schema uses thenotations and concepts

of the relational datamodel

Schema

Note: The terms Schema and Data Model are often used interchangeably as

in The ER Model for this application is given below


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Database design isrealized using aschema

Schema is only onebut a very significantcomponent ofdatabase design

DatabaseDesign


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Database is therepository that storesdata

The structure of therepository conformswith a particulardatabase design

Database


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Mapping meanstransforming a schemadefined using one

model into a schemadefined using anothermodel

Example: Mapping

between an ER schemaand a relational schema

Mapping


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A simplified meta-model

Data Model Abstraction

Schema DB Design

Database

Mapping

Defines-rules-for

creates

instantiates

Part-of

organizes

provides


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Elements of Data Models - 1

Define the terminology and semantics forstructuring entities, attributes and relatedmeta-data E.g., Relation, Attribute, Primary Key,

ForeignKey, etc.

Define the mechanism for manipulatingthe data using the data model Data Definition Language (DDL)

Data Manipulation Language (DML)

Define a set of rules for mapping from onemodel to another (e.g., ER to relational)


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Elements of Data Models - 2

Enable multiple levels of abstraction(e.g., physical, logical, conceptual)

Enable compact, efficient and secure

implementations

Why not one

universal

data model?


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Who is it for, anyway?

Business user/ Owner

To get a view of what business datais available without getting boggeddown with large data dumps

Software

Designer

To be able to design applications

that access the dataSoftwareDeveloper

To be able to build softwareprograms that help business usersmanipulate the data

SoftwareMaintenanceEngineer

To easily understand any issueswith data or underlying softwarewithout getting bogged down withactual data representations


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So what is the big deal?!

Lets discuss issues with data modeling inthe following domains

Employee Information

Organization hierarchy

Network infrastructure

Engine design (Aircraft, automobile, etc.)

Building plan

Road infrastructure

Genetics

Rich media

Think againWould one size

fit all?


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A Quick Review of DB Layers

Physical Schema

Logical Schema

External Schema

Note: We will revisiting this important concept as we go along


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The Big Picture Enterprise Architecture

Contextual

Conceptual

Logical

Physical

As Built

Functioning

Contextual

Conceptual

Logical

Physical

As Built

Functioning

Why

Why

Who

Who

When

When

Where

Where

What

What

How

How

Contextual

Conceptual

Logical

Physical

As Built

Functioning

Contextual

Conceptual

Logical

Physical

As Built

Functioning

Why

Why

Who

Who

When

When

Where

Where

What

What

How

How

Contextual

Conceptual

Logical

Physical

As Built

Functioning

Contextual

Conceptual

Logical

Physical

As Built

Functioning

Why

Why

Who

Who

When

When

Where

Where

What

What

How

How

Contextual

Conceptual

Logical

Physical

As Built

Functioning

Contextual

Conceptual

Logical

Physical

As Built

Functioning

Why

Why

Who

Who

When

When

Where

Where

What

What

How

How

Source: The Zachman Framework

01-data modeling intro_v2

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