CHAPTER 1: Core Concepts and Evolution of
Database Systems
1. Introduction: The Ubiquity and Value of Data and Databases
In today's world, data is ubiquitous (abundant, global, everywhere) and pervasive (unescapable, prevalent,
persistent). Organizations rely heavily on data to manage day-to-day operations and, critically, to generate
information, which in turn is the basis for good decisions. Databases are presented as the best way to store
and manage data due to their ability to make data persistent and shareable in a secure way. Modern
businesses, from telecommunications companies managing trillions of phone calls to search engines like
Google responding to over 91 million searches per day, fundamentally depend on databases for rapid storage,
management, and retrieval of vast amounts of data. A deep understanding of database structures and their
proper use is therefore vital for any information systems professional.
2. Data vs. Information vs. Knowledge
A crucial distinction for understanding database design is the difference between data, information, and
knowledge:
● Data: Consists of raw facts that have not yet been processed to reveal their meaning. For example,
individual entries of faculty data in a database are raw facts.
● Information: Is the result of processing raw data to reveal its meaning. This processing can range
from simple organization to complex statistical modeling, and requires context to be meaningful.
Information serves as the foundation for decision making.
● Knowledge: Represents the body of information and facts about a specific subject. It implies
familiarity, awareness, and understanding of information as it applies to an environment. A key
characteristic is that new' knowledge can be derived from 'old' knowledge.
Key Takeaways:
● Data constitutes the building blocks of information.
● Information is produced by processing data.
● Information is used to reveal the meaning of data.
● Accurate, relevant, and timely information is the key to good decision making.
● Good decision making is the key to organizational survival in a global environment.
3. Introducing the Database and Database Management Systems
(DBMS)
An efficient data management typically requires the use of a computer database.
● Database: A shared, integrated computer structure that stores a collection of... End-user data—that
is, raw facts of interest to the end user and Metadata, or data about data. Metadata describes data
characteristics (e.g., data element names, value types, whether empty) and relationships, effectively
making a database a collection of self-describing data.
, ● Database Management System (DBMS): A collection of programs that manages the database
structure and controls access to the data stored in the database. It acts as the intermediary between
the user and the database, presenting a single, integrated view of the data.
3.1. Role and Advantages of the DBMS
The DBMS offers significant advantages over other data management methods:
● Improved Data Sharing: Enables multiple applications or users to access data.
● Improved Data Security: Provides a framework for better enforcement of data privacy and security
policies.
● Better Data Integration: Promotes a clearer view of the big picture of organizational operations.
● Minimized Data Inconsistency: Significantly reduces instances where different versions of the same
data appear in different places.
● Improved Data Access: Facilitates quick answers to ad hoc queries, which are spur-of-the-moment
question[s] to the database.
● Improved Decision Making: Better data quality (accuracy, validity, timeliness) leads to better
information, thus better decisions are based on it.
● Increased End-User Productivity: Empowers users with readily available data and tools to transform
it into usable information.
3.2. Types of Databases
Databases can be classified based on several criteria:
● Number of Users:
○ Single-user database: Supports one user at a time (e.g., a desktop database).
○ Multiuser database: Supports multiple concurrent users.
○ Workgroup database: Supports a small number of users (typically <50) or a specific
department.
○ Enterprise database: Used by the entire organization, supporting hundreds of users.
● Location:
○ Centralized database: Data located at a single site.
○ Distributed database: Data spread across several different sites.
○ Cloud database: Created and maintained using cloud data services (e.g., Microsoft Azure,
Amazon AWS), abstracting the underlying infrastructure.
● Type of Data Stored:
○ General-purpose database: Contains a wide variety of data used in multiple disciplines.
○ Discipline-specific database: Data focused on specific subject areas (e.g., financial, GIS,
medical).
● Intended Data Usage (Most Popular Classification Today):
○ Operational database (OLTP, Transactional, Production): Designed to support a company’s
critical day-to-day operations and immediate recording of transactions.
○ Analytical database: Focuses on storing historical data and business metrics for tactical or
strategic decision making. Often includes:
○ Data warehouse: A specialized database optimized for decision support, containing historical
data.
, ○ Online Analytical Processing (OLAP): Tools for advanced data analysis from the data
warehouse, forming the basis of business intelligence.
● Degree of Data Structure:
○ Unstructured data: Raw data in its original collected format, not immediately suitable for
information processing.
○ Structured data: Data formatted to facilitate storage, use, and information generation (e.g.,
tabular data).
○ Semistructured data: Partially processed data, often using formatting tags (e.g., XML data),
but not conforming to strict tabular formats.
● XML database: Supports storage and management of semistructured XML data.
● NoSQL database: A new generation of DBMS that is not based on the traditional relational database
model, designed to handle the unprecedented volume of data, variety of data types and structures,
and velocity of data operations characteristic of social media and Big Data.
4. The Importance of Database Design
Database design refers to the activities that focus on the design of the database structure that will be used to
store and manage end-user data. It is a crucial aspect because even a good DBMS will perform poorly with a
badly designed database.
Problems with Poor Database Design (using a multi-skill employee example):
● Difficulty in retrieving specific information (e.g., alphabetical employee listings, counts of certified
employees).
● Data Redundancy: Storing the same information multiple times (e.g., skill names with each
employee, agent info in multiple files). This leads to:
○ Poor data security.
○ Data Inconsistency: Different and conflicting versions of the same data.
○ Data-entry errors.
● Data Integrity Problems: The overall correctness and consistency of data (e.g., entering a
non-existent agent).
○ Data Anomalies: Abnormal conditions arising from redundant data that lead to integrity
issues.
○ Update anomalies: Needing to change the same data in multiple locations.
○ Insertion anomalies: Needing to add dummy entries to store new, unrelated data.
○ Deletion anomalies: Deleting valid data inadvertently when deleting related records.
● Structural rigidity: Requiring changes to the database structure (adding columns) for new data
instances (e.g., an employee gaining a fourth skill).
A well-designed database facilitates data management and generates accurate and valuable information. A
poorly designed database is likely to become a breeding ground for difficult-to-trace errors that may lead to
poor decision making, and poor decision making can lead to the failure of an organization.
5. Evolution from File Systems to Database Systems
Understanding databases is enhanced by examining the limitations of their predecessors: file systems.
, 5.1. File System Data Processing (Manual and Computerized)
Historically, data was managed through paper-and-pencil manual systems. With organizational growth,
computerized file systems emerged, where data was stored in independent files (e.g., CUSTOMER file, AGENT
file). DP (Data Processing) specialists created programs to retrieve, manipulate, and report data from these
files.
5.2. Problems with File System Data Processing
Despite being an improvement, file systems suffered from severe limitations:
● Lengthy Development Times: Even simple data retrieval required extensive, procedural programming
(what must be done and how to do it).
● Difficulty of Getting Quick Answers (Ad Hoc Queries): Impractical due to the need for custom
programming for every query.
● Complex System Administration: Each file required its own management programs, leading to a
proliferation of programs, especially as different departments owned their data in separate files
(islands of information).
● Lack of Security and Limited Data Sharing: Security features were difficult to implement and often
insufficient for robust data sharing.
● Extensive Programming for Changes: Any modification to a file structure (e.g., adding a field) or data
characteristics (e.g., changing data type) required modifying all of the programs that use the data in
that file. This led to:
● Structural Dependence: Access to a file is dependent on its structure; changes require program
modification.
● Data Dependence: Programs are affected by changes in data storage characteristics.
● Data Redundancy: The same data stored unnecessarily in different locations, leading to the data
inconsistency, data-entry errors, and data integrity problems detailed above.
● Data Anomalies: Update, insertion, and deletion anomalies were common due to widespread data
redundancy.
5.3. File System Redux: Modern End-User Productivity Tools
The text notes that modern tools like spreadsheets, while useful for data analysis, are often misused as
substitutes for databases. When users create separate copies of data in spreadsheets, it replicates the same
problems as the file systems created by the early DP specialists. Spreadsheets lack self-documentation
through metadata, enforcement of data types or domains... defined relationships among tables, or
constraints.
6. The Database System Environment and DBMS Functions
The database system is a complete environment designed to overcome file system weaknesses. It consists of
logically related data stored in a single logical data repository.
6.1. The Database System Environment Components:
The database system is composed of five major parts:
● Hardware: Physical devices (computers, storage, network devices).
Database Systems
1. Introduction: The Ubiquity and Value of Data and Databases
In today's world, data is ubiquitous (abundant, global, everywhere) and pervasive (unescapable, prevalent,
persistent). Organizations rely heavily on data to manage day-to-day operations and, critically, to generate
information, which in turn is the basis for good decisions. Databases are presented as the best way to store
and manage data due to their ability to make data persistent and shareable in a secure way. Modern
businesses, from telecommunications companies managing trillions of phone calls to search engines like
Google responding to over 91 million searches per day, fundamentally depend on databases for rapid storage,
management, and retrieval of vast amounts of data. A deep understanding of database structures and their
proper use is therefore vital for any information systems professional.
2. Data vs. Information vs. Knowledge
A crucial distinction for understanding database design is the difference between data, information, and
knowledge:
● Data: Consists of raw facts that have not yet been processed to reveal their meaning. For example,
individual entries of faculty data in a database are raw facts.
● Information: Is the result of processing raw data to reveal its meaning. This processing can range
from simple organization to complex statistical modeling, and requires context to be meaningful.
Information serves as the foundation for decision making.
● Knowledge: Represents the body of information and facts about a specific subject. It implies
familiarity, awareness, and understanding of information as it applies to an environment. A key
characteristic is that new' knowledge can be derived from 'old' knowledge.
Key Takeaways:
● Data constitutes the building blocks of information.
● Information is produced by processing data.
● Information is used to reveal the meaning of data.
● Accurate, relevant, and timely information is the key to good decision making.
● Good decision making is the key to organizational survival in a global environment.
3. Introducing the Database and Database Management Systems
(DBMS)
An efficient data management typically requires the use of a computer database.
● Database: A shared, integrated computer structure that stores a collection of... End-user data—that
is, raw facts of interest to the end user and Metadata, or data about data. Metadata describes data
characteristics (e.g., data element names, value types, whether empty) and relationships, effectively
making a database a collection of self-describing data.
, ● Database Management System (DBMS): A collection of programs that manages the database
structure and controls access to the data stored in the database. It acts as the intermediary between
the user and the database, presenting a single, integrated view of the data.
3.1. Role and Advantages of the DBMS
The DBMS offers significant advantages over other data management methods:
● Improved Data Sharing: Enables multiple applications or users to access data.
● Improved Data Security: Provides a framework for better enforcement of data privacy and security
policies.
● Better Data Integration: Promotes a clearer view of the big picture of organizational operations.
● Minimized Data Inconsistency: Significantly reduces instances where different versions of the same
data appear in different places.
● Improved Data Access: Facilitates quick answers to ad hoc queries, which are spur-of-the-moment
question[s] to the database.
● Improved Decision Making: Better data quality (accuracy, validity, timeliness) leads to better
information, thus better decisions are based on it.
● Increased End-User Productivity: Empowers users with readily available data and tools to transform
it into usable information.
3.2. Types of Databases
Databases can be classified based on several criteria:
● Number of Users:
○ Single-user database: Supports one user at a time (e.g., a desktop database).
○ Multiuser database: Supports multiple concurrent users.
○ Workgroup database: Supports a small number of users (typically <50) or a specific
department.
○ Enterprise database: Used by the entire organization, supporting hundreds of users.
● Location:
○ Centralized database: Data located at a single site.
○ Distributed database: Data spread across several different sites.
○ Cloud database: Created and maintained using cloud data services (e.g., Microsoft Azure,
Amazon AWS), abstracting the underlying infrastructure.
● Type of Data Stored:
○ General-purpose database: Contains a wide variety of data used in multiple disciplines.
○ Discipline-specific database: Data focused on specific subject areas (e.g., financial, GIS,
medical).
● Intended Data Usage (Most Popular Classification Today):
○ Operational database (OLTP, Transactional, Production): Designed to support a company’s
critical day-to-day operations and immediate recording of transactions.
○ Analytical database: Focuses on storing historical data and business metrics for tactical or
strategic decision making. Often includes:
○ Data warehouse: A specialized database optimized for decision support, containing historical
data.
, ○ Online Analytical Processing (OLAP): Tools for advanced data analysis from the data
warehouse, forming the basis of business intelligence.
● Degree of Data Structure:
○ Unstructured data: Raw data in its original collected format, not immediately suitable for
information processing.
○ Structured data: Data formatted to facilitate storage, use, and information generation (e.g.,
tabular data).
○ Semistructured data: Partially processed data, often using formatting tags (e.g., XML data),
but not conforming to strict tabular formats.
● XML database: Supports storage and management of semistructured XML data.
● NoSQL database: A new generation of DBMS that is not based on the traditional relational database
model, designed to handle the unprecedented volume of data, variety of data types and structures,
and velocity of data operations characteristic of social media and Big Data.
4. The Importance of Database Design
Database design refers to the activities that focus on the design of the database structure that will be used to
store and manage end-user data. It is a crucial aspect because even a good DBMS will perform poorly with a
badly designed database.
Problems with Poor Database Design (using a multi-skill employee example):
● Difficulty in retrieving specific information (e.g., alphabetical employee listings, counts of certified
employees).
● Data Redundancy: Storing the same information multiple times (e.g., skill names with each
employee, agent info in multiple files). This leads to:
○ Poor data security.
○ Data Inconsistency: Different and conflicting versions of the same data.
○ Data-entry errors.
● Data Integrity Problems: The overall correctness and consistency of data (e.g., entering a
non-existent agent).
○ Data Anomalies: Abnormal conditions arising from redundant data that lead to integrity
issues.
○ Update anomalies: Needing to change the same data in multiple locations.
○ Insertion anomalies: Needing to add dummy entries to store new, unrelated data.
○ Deletion anomalies: Deleting valid data inadvertently when deleting related records.
● Structural rigidity: Requiring changes to the database structure (adding columns) for new data
instances (e.g., an employee gaining a fourth skill).
A well-designed database facilitates data management and generates accurate and valuable information. A
poorly designed database is likely to become a breeding ground for difficult-to-trace errors that may lead to
poor decision making, and poor decision making can lead to the failure of an organization.
5. Evolution from File Systems to Database Systems
Understanding databases is enhanced by examining the limitations of their predecessors: file systems.
, 5.1. File System Data Processing (Manual and Computerized)
Historically, data was managed through paper-and-pencil manual systems. With organizational growth,
computerized file systems emerged, where data was stored in independent files (e.g., CUSTOMER file, AGENT
file). DP (Data Processing) specialists created programs to retrieve, manipulate, and report data from these
files.
5.2. Problems with File System Data Processing
Despite being an improvement, file systems suffered from severe limitations:
● Lengthy Development Times: Even simple data retrieval required extensive, procedural programming
(what must be done and how to do it).
● Difficulty of Getting Quick Answers (Ad Hoc Queries): Impractical due to the need for custom
programming for every query.
● Complex System Administration: Each file required its own management programs, leading to a
proliferation of programs, especially as different departments owned their data in separate files
(islands of information).
● Lack of Security and Limited Data Sharing: Security features were difficult to implement and often
insufficient for robust data sharing.
● Extensive Programming for Changes: Any modification to a file structure (e.g., adding a field) or data
characteristics (e.g., changing data type) required modifying all of the programs that use the data in
that file. This led to:
● Structural Dependence: Access to a file is dependent on its structure; changes require program
modification.
● Data Dependence: Programs are affected by changes in data storage characteristics.
● Data Redundancy: The same data stored unnecessarily in different locations, leading to the data
inconsistency, data-entry errors, and data integrity problems detailed above.
● Data Anomalies: Update, insertion, and deletion anomalies were common due to widespread data
redundancy.
5.3. File System Redux: Modern End-User Productivity Tools
The text notes that modern tools like spreadsheets, while useful for data analysis, are often misused as
substitutes for databases. When users create separate copies of data in spreadsheets, it replicates the same
problems as the file systems created by the early DP specialists. Spreadsheets lack self-documentation
through metadata, enforcement of data types or domains... defined relationships among tables, or
constraints.
6. The Database System Environment and DBMS Functions
The database system is a complete environment designed to overcome file system weaknesses. It consists of
logically related data stored in a single logical data repository.
6.1. The Database System Environment Components:
The database system is composed of five major parts:
● Hardware: Physical devices (computers, storage, network devices).
