1. Introduction #

Data, Info, Knowledge, Wisdom #

Data is a collection of points with no discernible meaning.

Information is data in context.

Knowledge is information that has been assimilated in the mind.

Wisdom is the application of knowledge.

What is a Database? #

A database is a shared, integrated computer structure that houses:

• End user data (raw facts)
• Metadata (data about data)

Database Systems #

A database system consists of:

• Data (the database)
• Software
• Hardware
• Users

Database systems allow users to Store, Update, Retrieve, Organise, and Protect their data.

Database Users #

• End users: Use the database system to achieve some goal
• Application developers: Write software to allow end users to interface with the database system
• Database Administrator (DBA): Creates or modifies the schema, and designs and manages the database system
• Database systems programmer: Writes the database software itself

Uses of Databases #

• Businesses: Inventory, order processing, payroll, accounting, shipping
• Education: Educational institutions use databases to keep track of students, grades, transfers, transcripts, and other student data
• Non-Profits: Many charities and other non-profit groups use a database to store details of donations, volunteers, hours served in the community, clients helped, and other information related to the organisation
• Household and Family Management: Many people use databases to keep track of family birthdays, bills and expenses in the home, addresses of friends and relatives, and DVD collections

Each time you make a purchase and the sales clerk asks for your address or email, your information is kept and stored on a customer database. These collections of data are used to send mailings of special offers, discounts, and other deals.

Database Management Systems (DBMS) #

A DBMS is a collection of programs that manages database structure and cotnrols access to data.

A DBMS allows:

• Data to be shared among multiple applications or users
• Data management to be more efficient and effective

Advantages of s DBMS #

A DBMS solves many of the problems encountered in data management:

• Improved data sharing
• Improved data security
• Better data integration
• Minimised data inconsistency
• Improved data access
• Improved decision making
• Increased end-user productivity

File Based Systems #

• Data is stored in files
• Each file has a specific format
• Programs that use those files depend on knowledge about that format
• Disadvantages:
  • No standards
  • Data duplication
  • Data dependence
  • Inconsistent data
  • Inflexibility
  • Limited data sharing
  • No way to generate ad-hoc queries
  • No provision of security, recovery, concurrency, etc.

Data Models #

A data model is a collection of concepts for describing data.

A schema is a description of a particular collection of data, using a given data model.

The relational model of data is the most widely used model today:

• Relation: The main concept, basically a table with rows and columns
• Scheme: Every relation has a schema, which describes the columns, or fields

Levels of Abstraction #

Schemas are defined using Data Definition Language (DDL), data is modified/queries using Data Manipulation Language (DML).

• Views descrbe how users see the data
• Conceptual schema defines a logical structure
• Physical schema describes the files and indices used

View Level or External Schema #

• Tells us how the data should be shown to the user
• Different users will have a different view according to the authorisation they have
• Different views increase the security of the system

Conceptual Level or Logical Level #

• Tells us how the data is actually stored and structured
• The data could be stored using different data models
• How the tables are related to each other
• The conceptual level creates a blueprint of the data

Physical Level or Internal Schema #

• Tells us where the data is actually stored
• It tells us the actual location of the data that is being stored by the user
• The Database Adminsitrators (DBA) decide:
  • which data should be kept at which particular disk drive
  • how the data has to be fragmented, where it has to be stored, etc.
  • if the data has to be centralised or distributed

Types of Data Models #

• Hierachial Model
• Network Model
• Relational Model
• Object-oriented Data Model
• Entity-relationship Model
• NoSQL