Informatics Lecture 9

Database

A structured collection of data that can be easily accessed, managed, and updated.

Database Management System (DBMS)

A group of programs that manipulate the database and provide an interface between the database and its users.

Database Administrator (DBA)

A skilled IS professional who directs all activities related to an organization’s database.

Byte

A unit of data made up of eight bits.

Character

The basic building block of information.

Field

A name, number, or combination of characters that describes a business object or activity.

Record

A collection of related data fields.

File

A collection of related records.

Entity

A person, place, or thing for which data is collected, stored, and maintained.

Attribute

A characteristic of an entity.

Data Item

The specific value of an attribute.

Primary Key

A field or set of fields that uniquely identifies a record.

Data Model

A diagram of data entities and their relationships.

Enterprise Data Modelling

Investigates the general data and information needs of the organization at the strategic level.

Entity-Relationship (ER) Diagrams

Data models that use graphical symbols to show organization entities and relationships.

Relational Model

Organizes data into collections of two-dimensional tables called relations.

Domain

The range of allowable values for a data attribute.

Data Cleansing/Cleanup

The process of detecting and correcting or deleting incomplete or inaccurate records in a database.

Data Center

A climate-controlled building that houses database servers and systems delivering mission-critical information.

Traditional Data Centers

Warehouses filled with server racks and cooling systems.

Flat File Database

A simple database program with records that have no relationships to one another.

Single User Database

A database that allows only one person to use it at a time (e.g., Access).

Multiple User Database

A database that allows many users to access it simultaneously (e.g., SQL Server, Oracle).

Schema

A description of the entire database, which can be part of the database or a separate file.

DBMS in User View

References a schema to access requested data in relation to other data.

Data Definition Language (DDL)

Instructions used to define and describe data and relationships in a database.

Data Dictionary

A detailed description of all data used in the database, including data flows and organization.

Concurrency Control

Manages situations where multiple users need to access the same record simultaneously.

Data Manipulation Language (DML)

A language provided with a DBMS that allows users to access and modify data.

Structured Query Language (SQL)

The standard query language for relational databases, adopted by ANSI.

Big Data

Enormous datasets generated by web and mobile applications, often measured in terabytes and petabytes.

Data Management

A set of functions defining how data is obtained, stored, secured, and processed to meet user needs.

Data Governance

Defines roles and processes for ensuring data trustworthiness and usability across an organization.

Data Lifecycle Management (DLM)

A policy-based approach for managing the flow of enterprise data.

Data Warehouse

A large database that collects business information from various sources to support decision-making.

ETL

Stands for Extract, Transform, Load; a process used in data warehousing.

Data Mart

A subset of a data warehouse used by smaller businesses or departments for decision-making.

Non-Relational Databases (NoSQL)

Databases that store and retrieve data using methods other than two-dimensional tables.

Key-Value NoSQL

Databases with two columns ("key" and "value") for storing data.

Document NoSQL

Databases for managing document-oriented information, such as social media posts.

Graph NoSQL

Databases for analyzing relationships among various data points, suitable for social media data.

Column NoSQL

Databases that store data in columns for fast response times with large data volumes.

Hadoop

An open-source framework for storing and processing large datasets.

Function of data management

• without data and the ability to process the data, an organization could not successfully complete most business activities.

• To transform data into useful information

Database Approach to Data Management

(1) Information systems share a pool of related data. (2) Offers the ability to share data and information resources. (3) A database management system (DBMS) is required.

Characteristics of Traditional Approach

Each distinct operational system used data files dedicated to that system.

Considerations for Building a Database

Content, access, logical structure, physical organization, and security.

Relational Model Structure

Rows represent data entities (records), and columns represent attributes (fields).

Data Manipulation Methods

Selecting, projecting, joining, and linking data from tables.

Types of databases

flat file, single user, and multiple users

flat file

Simple database program whose records have no relationship to one another

single user

Only one person can use a database at a time.

multiple users

Allow dozens or hundreds of people to access the same database system at the same time.

how is data sorted and retrieved?

When an application program needs data, it requests the data through the DBMS.

characteristics of Daas

1. stored on service provider’s servers

2. accessed by the client over the network

3. data administration is handled by service provider

characteristics of big data

Velocity, volume, variety, veracity, and value

characteristics of big data

Volume: It indicates to the size of data. Analyzing data with very large volume to extract valuable information is one of important challenges of big data.

Velocity: The term velocity is referring to the speed of data. Flooding of data is very high speed, and it has to be dealt with in appropriate time.

Variety: The data is very diverse and has many types as it comes from different sources with different structures such as: social data, audio, video unstructured data, email and etc.

Value: Another challenging issue is to convert the data into values to understand and discover hidden values.

Veracity: Data veracity, in general, is how accurate or truthful a data set may be. More specifically, when it comes to the accuracy of big data, it’s not just the quality of the data itself but how trustworthy the data source, type, and processing of it is.

challenges of big data

1. data capture, storage, visualization, analysis, and updating data securely.

2. Analyzing data with very large volume to extract valuable information

elements of data warehouse

1. A relational database to store and manage data.

2. An extraction, loading, and transformation (ELT) solution for preparing the data for analysis.

3. Analysis tools, reporting, and data mining capabilities.

4. Client analysis tools for visualizing and presenting data to business users.

advantages of NoSQL databases

• Ability to spread data over multiple servers so that each server contains only a subset of the total data.

• Do not require a predefined schema.

primary components of Hadoop

• A data processing component (MapReduce).

• A distributed file system (Hadoop Distributed File System, HDFS).