This document serves as an introduction to data and databases, outlining key concepts such as data, information, knowledge, and the significance of databases in managing data resources. It explores big data characteristics, relational databases, and database management systems (DBMS), alongside basics of SQL for data manipulation. The content emphasizes the importance of understanding these elements for effective data management and decision-making.

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BATO INSTITUTE OF SCIENCE AND TECHNOLOGY, INC.
Dolho, Bato, Leyte
HOSPITALITY MANAGEMENT DEPARTMENT
GE ELECT 1 – LIVING IN THE I.T. ERA
MODULE 4. DATA AND DATABASES
Introduction
Welcome to the module on Data and Databases. In this module, we will explore the fundamental concepts of
data, information, and knowledge, and understand the importance of databases for managing data resources.
Additionally, we will delve into the concept of big data, various types of databases, and the basics of SQL.
1. Data, Information, and Knowledge
1.1 Data
• Definition: Data consists of raw, unprocessed facts and figures without context. It can include
numbers, text, images, and other types of raw input.
• Examples:
o A list of customer names and their contact numbers.
o A series of numerical values like 25, 30, 35, 40.
1.2 Information
• Definition: Information is data that has been processed, organized, and structured to be meaningful
and useful. It provides context and relevance to raw data.
• Examples:
o A report showing the average age of customers based on the numerical data.
o A summary of customer contact information categorized by region.
1.3 Knowledge
• Definition: Knowledge is derived from information through analysis and interpretation. It involves
understanding patterns and making informed decisions based on information.
• Examples:
o Using customer age data to create marketing strategies tailored to different age groups.
o Predicting future customer needs based on historical purchasing patterns.

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Why Understanding These Concepts is Important:
• Data: Represents the raw input collected from various sources.
• Information: Transforms data into something that has meaning and can be used for decision-making.
• Knowledge: Utilizes information to gain insights and make informed decisions.
2. Big Data
2.1 Definition
• Big Data: Refers to extremely large datasets that are complex and difficult to process using traditional
data processing tools.
2.2 Characteristics
• Volume: Refers to the enormous amount of data generated every second. For example, social media
platforms generate terabytes of data daily.
• Velocity: Refers to the speed at which data is generated and processed. For instance, real-time data
streaming from sensors.
• Variety: Refers to the different types of data formats, such as structured data (tables), semi-structured
data (XML, JSON), and unstructured data (text, images).
2.3 Difference from Traditional Data
• Traditional Data: Often structured and manageable with conventional tools and databases.
• Big Data: Requires advanced technologies and tools (e.g., Hadoop, Spark) to handle its scale and
complexity.
Why It Matters:
• Scalability: Big Data technologies allow organizations to process vast amounts of information
efficiently.
• Insights: Analyzing big data can reveal patterns and trends that inform strategic decisions.
3. Databases
3.1 Data Model and Relational Database
• Data Model: A framework that defines the structure of data, including how data is stored, organized,
and manipulated.
• Relational Database: A type of database that uses tables to represent data and relationships between
data. Each table consists of rows (records) and columns (fields).
o Example: A table for storing employee information with columns for Employee ID, Name,
Department, and Salary.

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3.2 Designing a Database
• Schema: The blueprint of a database that outlines the structure, including tables, fields, and
relationships.
• Normalization: The process of organizing data to reduce redundancy and ensure data integrity. It
involves dividing a database into tables and defining relationships to minimize duplication.
o Example: Splitting a customer table into separate tables for customer details and orders.
3.3 Data Types
• Definition: The types of data that can be stored in a database, such as integers, floating-point numbers,
strings, dates, and more.
o Example: An integer data type for storing age, and a string data type for storing names.
Why It Matters:
• Efficient Storage: Proper database design and normalization improve data storage efficiency and
reduce redundancy.
• Data Integrity: Ensures that data remains accurate and consistent.
4. Database Management System (DBMS)
4.1 Definition
• DBMS: Software that manages and organizes databases, allowing users to store, retrieve, and
manipulate data efficiently.
4.2 Functions
• Data Storage: Stores data in an organized manner.
• Data Retrieval: Provides mechanisms for querying and accessing data.
• Data Security: Controls access to data and ensures that unauthorized users cannot access or modify
it.
Why It Matters:
• Centralized Management: A DBMS provides a central repository for data, making it easier to manage
and maintain.
• Scalability: DBMSs can handle large volumes of data and complex queries.

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5. SQL (Structured Query Language)
5.1 Definition
• SQL: A language used to interact with relational databases. It allows users to perform operations such
as querying, updating, and managing data.
5.2 Basic SQL Commands
• SELECT: Retrieves data from a database.
o Example: SELECT * FROM Employees; retrieves all data from the Employees table.
• INSERT: Adds new records to a database.
o Example: INSERT INTO Employees (Name, Age) VALUES ('John Doe', 30);
• UPDATE: Modifies existing records.
o Example: UPDATE Employees SET Age = 31 WHERE Name = 'John Doe';
• DELETE: Removes records from a database.
o Example: DELETE FROM Employees WHERE Name = 'John Doe';
Why It Matters:
• Data Manipulation: SQL provides powerful tools to manage and manipulate data effectively.
• Query Efficiency: Enables users to perform complex queries and analyses on data.
6. Other Types of Databases
6.1 NoSQL Databases
• Definition: Databases designed for handling unstructured and semi-structured data. They offer flexible
data models and are suitable for large-scale applications.
o Example: MongoDB (document-oriented), Cassandra (column-family).
6.2 Object-Oriented Databases
• Definition: Databases that store data as objects, similar to how data is represented in object-oriented
programming.
o Example: db4o, ObjectDB.
Why It Matters:
• Flexibility: Different types of databases are tailored for various use cases and data requirements.
• Specialized Applications: NoSQL and object-oriented databases can handle specific data needs and
scale effectively.

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Conclusion
Understanding the distinctions between data, information, and knowledge, as well as the role of database
technology, is crucial for effective data management. This module has provided an overview of these concepts,
as well as practical insights into big data, database design, and SQL. Mastery of these topics will equip you
with the foundational knowledge necessary for managing and utilizing data resources efficiently.
Prepared by:
MARY JANE PAGAY-CIERVA, LPT, MSIT
Instructor