Option A: Database (CS SL)

Data

Raw fact that is meaningless on it’s own / unprocessed values

Information

meaningful data that has been interpreted / added context or meaning

Information system

Collection of hardware, software and human resources that work together to transform raw data into useful information

Database

Organized of data stored electronically and is a component of the information system, it is stored in tables as records (rows, data about one particular person / company / item in a database) and fields (column, part of a record and contains a single piece of data for the subject of the record)

Created and stored by a software called DMBS (SQLite, mySQL, SQLserver)

Queries / Commands

store, retrieve, delete and edit data

Why do we use database

• processing / querying data (search / sort / analyze)

• generate reports

• automatically support data validation / data verification when filling the database

• standardized (used in multiple systems)

• provides ability to sore a greater number of records compared to a spreadsheet

Data Validation

Checks that input follows rules for the type of input entered

Data Verifiction

Checks that input matches expected values in database

Entity

• a real world object / person

• represented by a row

• referred to as a record / tuple in a database

Table

• stores a set of related data called records

• every record has value

• records in each table share the same fields / types of data

Tuple

Collection of items that may or may not be related to one another

Collection of items that may or may not be related to one another

Primary Key

• identify a given record

• distinctive for each record

Foreign Key

• primary key field in another table

• connect record in one table to another

Data types

text, char, boolean, int, real, datetime

Secondary / Alternate Key

• values that can be used as primary key (PK cannot be NULL, but SK can)

• can have more than 1 SK

Candidate Key

• Identify each unique record in a database

• both PK and SK are candidate keys

Composite PK

when one field is not enough to uniquely identify records, but 2 fields together can work as a PK

example: name & birthday

Database Schema

• defines how data is organized

• overview of the database

• organizational chart

Relational Database

• tables are related to one another

• each table has a PK

• columns → attributes

• rows → records, if none then tuples

Referential Integrity

• row → identifier / PK

• relations between table are consistent and logical

Database Management System (DBMS)

set of programs that allow to read, store, change / extract data in a database

example: SQLite, mySQL, PostgreSQL

Components of DBMS

• data dictionary

• data safety

• query processor

• storage engine

• concurrency

• security

Data Dictionary

• file or set of files that store information about the database and the tables inside

• manage metadata / metadata repository

Data Safety

backup & recovery, data integrity

Query Processor

accept queries and return appropriate output

Storage Engine

handles, create, read, update and delete operations

Concurrency

allows multiple users to access database; make sure multiple user can’t modify the same data simultaneously

example: row - locking

Security

enforce user policies, include:

• access rights

• audit trails: changes made to the data

• data locking: lock rows that are accessed

• validation: make sure new data follows rules

• encryption

• back ups: update copies

Database Transactions

• collection of low - level tasks

• set of SQL statements executed sequentially

• all operations are performed and if there is an error, changes either made permanent or save changes commited if not partially or reverted or rolled back

ACID

• Atomicity: all task performed or none

• Consistency: all data written must be valid

• Isolation: no transaction will interfere

• Durability: once transaction is complete, changes are permanent even with system failure

Purpose of transaction

make sure changes don’t run into each other & is permanent

Data Integrity

• data should be what the user means it to be

• accuracy, completeness, validity

Data Redundancy

situation where the same piece of data is stored in 2 or more different place

pros:

• faster data access

• better protection

cons:

• data inconsistency (updated at one place)

• more storage used

Normalization

process where larger tables are divided into smaller tables while ensuring data integrity and reduce data redundancy

Why we use Normalization

• reduce data redundancy (storage)

• reduce table complexity (insertion, updates and deletion less error prone

• make sure data is stored logically (to make querying more efficient)

The 1st Normal Form (1NF)

• eliminate duplicate columns, multiple types of value

• create separate tables for each group

The 2nd Normal Forms (NF)

• meet requirements for 1NF

• eliminate partial dependency, 2PK, columns dependent on one attribute of a composite PK

The 3rd Normal Form

• meet requirements for 2NF

• eliminate transitive dependency (column depending on another column)

Advantages of Normalization

• less data storage required → lack of duplicates

• data is more likely to be consistent

• increased data security → easy to locate

• operations are conducted more quickly and efficiently due to table structure

• simpler queries

• easier to understand as it is logically organized

Anomalies

• prevented by 2NF

• three types of anomaly:

  • insertion anomaly: or cannot be inserted due to missing data

  • deletion anomaly: when certain attributes are lost due to deletion of other attributes

  • update anomaly: data only partially updates

Database Administrator (DBA)

ensure that data is performant, secure, and recoverable by: updating database, maintaining security, managing back up procedures, and establishing recovery plan

Database Definition Language (DDL)

• commands that allow us to define and modify the structure and metadata of a database

• generate data dictionary

• only available to DBAs

• DDL commands: alter, create, drop, truncate, rename

Data Modelling

• visual representation of a whole / part of an information system

• helps stakeholders have a shared understanding of a system

• Three types:

  • conceptual: rough sketch, entities

  • logical: 4 entities, attributes, FK

  • physical: data, data type, attributes, relationship

Advantages of Data Modelling

• avoid issues:

  • redundancy

  • lack of integrity

  • lack of consistency

• helps developers develop actual database

• lack → deficient modelling

ERD (Entity Relationship Diagram)