- Created by: Kevin Hodge
- Created on: 21-10-17 18:18
A database usually holds large collection of data organised especially for rapid search and retrieval.
A database is a logically coherent collection of data with some inherent meaning.
Data is raw facts and figures stored as independent units.
Information is data given context and meaning. It has been processed.
Data integrity is the maintenance of, and the assurance of the accuracy and consistency of, data over its entire life-cycle, and is a critical aspect to the design, implementation and usage of any system which stores, processes, or retrieves data.
Data à Information =
-E.g. Find Average
-E.g. work out VAT.
Databases can be used to share data with different computers and users.
It can also be used as reliable storage which can be accessed.
Efficiency and optimised.
Don’t need to care about physical formats.
They can also be used for communication.
File based system – redundancy and synchronisation issues.
File based systems are application focused.
Easy to create
Fast for specialised tasks
Centralised data definitions
Centralised access control
A database is a centralised store of data.
- Single repository of data
Independent of individual applications
- No one application dictates use
- Contains a description of itself
Designing the database:
Model data first – getting the requirements from users.
Design a single logical model of the data.
Identifies entities and relationships.
Database management software:
DBMS is the software which manages the database.
It sits between the application and the data.
- Making changes to the structure of the data
- Manipulating the data
- Accessing the data
People who work with DBMS:
- Database Admins
- Application developers
- End users
Types of DBMS:
- Relational (main one for us!)
- Object relational
- Data redundancy
- Data consistency
- Data integrity
- Adherence to standards
- Control of security and access
Idealised database development systems:
Databases are a part of information systems.
The database may be divided into levels/steps
- Conceptual level (understand)
- Logical level (design)
- Physical level (implement)
We may need modelling techniques:
- Entity-relationship models
- Relational data models
Definition à Requirements à Design à Implementation à Testing à Maintenance
Good data models:
- Structural validity
- Share ability
- Diagrammatic representation
File à Stores data in a system