Chapter3

Learning Outcomes

  • Define a database and a database management system.

  • Explain logical database design and the relational database model.

  • Define the components of a database management system.

  • Summarize recent trends in database design and use.

  • Explain the components and functions of a data warehouse.

Databases

Definition

  • Database: A collection of related data stored in a central or multiple locations.

  • Data Hierarchy: Structure and organization involving fields, records, and files.

  • Database Management System (DBMS): Software that enhances efficiency in creating, storing, maintaining, and accessing database files.

Interaction with User, DBMS, and Database

  • User interacts with the DBMS to perform data operations.

Types of Data in a Database

Internal Data

  • Collected within an organization and stored in internal databases.

External Data

  • Gathered from various external sources and stored in a data warehouse.

Methods for Accessing Files

Sequential Access

  • Records are organized and processed in numerical order based on a primary key.

  • Typically used for backup and archive files.

Random Access

  • Records can be accessed in any order.

  • Suitable for processing a small number of records at a time.

Indexed Sequential Access Method (ISAM)

  • Provides options for accessing records either sequentially or randomly based on the access requirements.

Logical Database Design

Views of Information in a Database

  • Physical View: Concerned with how data is stored and retrieved from storage media.

  • Logical View: How information is organized and presented to users; could vary for different users.

Data Model

  • Defines how data is created, represented, organized, and maintained.

  • Includes structure, operations, and integrity rules.

Common Data Models

Hierarchical Model

  • Represents relationships in a tree-like structure (nodes and branches).

Network Model

  • Allows multiple parent-child relationships.

Relational Model

  • Comprises tables (two-dimensional) with rows (records) and columns (fields), alongside definitions in a data dictionary.

Relational Model Keys

Primary Key

  • Uniquely identifies each record in the database.

Foreign Key

  • Links to another table's primary key for cross-referencing.

Normalization

  • Process to eliminate redundant data and ensure relevant data storage.

  • Staged improvement from first normal form (1NF) to fifth normal form (5NF).

DBMS Components

  • Database Engine: Handles storage, manipulation, and retrieval operations.

  • Data Definition: Defines structure and schema of database files.

  • Data Manipulation: Allows addition, deletion, modification, and retrieval through query languages.

  • Application Generation: Designs application elements using database data.

  • Data Administration: Manages backup, recovery, security, and permissions.

Recent Trends in Database Design and Use

  • Data-driven websites, natural language processing, distributed databases, and object-oriented databases.

  • Advances in artificial intelligence impacting functionalities.

Data Warehouses

Definition

  • Collections of data from various sources designed to support decision-making.

  • Also referred to as hypercubes, as they store multidimensional data.

Characteristics

  • Subject-oriented, integrated, time-variant, and capable of capturing aggregated data for analytics.

Storage in Data Warehouses

  • Raw Data: Original state of collected information.

  • Summary Data: Contains subtotals across different categories.

  • Metadata: Data about data’s origin, quality, and other characteristics.

Output from Data Warehouses

  • Utilizes Online Analytical Processing (OLAP) for multidimensional analysis generating business intelligence.

  • Data-mining analysis identifies patterns and relationships within data.

Benefits of Data Warehouses

  • Cross-reference operations for comparative analysis.

  • Rapid report generation using aggregated data from multiple sources.

  • Support for extensive historical data analysis aiding management decisions across varying demands.

Data Marts

Definition

  • Smaller, focused versions of data warehouses serving specific departments.

Advantages

  • Faster access, improved response time, ease of creation, and cost-effectiveness.

Disadvantages

  • Limited scope and challenges in consolidating data across departments.

Business Analytics (BA)

Purpose

  • Employs data and statistical methods to extract insights for decision-makers.

Methods

  • Descriptive Analytics: Reviews past events to inform future actions.

  • Predictive Analytics: Prepares users for future occurrences.

  • Prescriptive Analytics: Suggest courses of action using data-driven insights.

Big Data

Definition

  • Large volume of data that exceeds the ability of traditional processing methods.

The 3 Vs

  • Volume: Transaction quantity.

  • Variety: Mix of structured and unstructured data.

  • Velocity: Speed of data gathering and processing.

Applications and Risks

  • Provides competitive advantage in sectors like retail, finance, and healthcare, but poses privacy risks including discrimination and ethical concerns.

Commonly Used Platforms for Big Data Analytics

  • Apache Hadoop, NoSQL, Cassandra, and commercial platforms like SAP Big Data Analytics and Tableau.

Database Marketing

Definition

  • Utilizes customer data for effective promotion and strategies.

Techniques

  • Involves data segmentation, multivariate analysis, and automated tools.

Goals

  • Focused on increasing profits by transforming marketing to a proactive approach and engaging customers through targeted strategies.