2. Database Overview

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Last updated 1:52 AM on 10/19/26
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57 Terms

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File-Based System

Stores data in separate files managed by individual applications.

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Data

Raw facts that have not been processed.

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Field

Single piece of data, such as a customer name.

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Record

Collection of related fields describing a person, place, or thing.

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File

Collection of related records.

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Data Redundancy

Unnecessary duplication of data in multiple locations.

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Data Inconsistency

Different values for the same data across locations.

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Data Dependence

Changes in data storage characteristics affect programs.

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Data Independence

Data storage characteristics can change without affecting programs.

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Structural Dependence

Changing a file's structure requires program changes.

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Structural Independence

File structure can change without affecting programs.

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File-Based System Limitations

Data redundancy, inconsistency, limited sharing/security, dependence, and lengthy development.

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Data Anomaly

Problem caused when changes to redundant data are not completed correctly.

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Insertion Anomaly

Cannot add new data without adding unrelated information.

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Update Anomaly

Same information must be updated in multiple places.

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Deletion Anomaly

Deleting data unintentionally removes other important information.

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Database

Shared, integrated computer structure that stores data.

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End-User Data

Raw facts that are important to users.

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Metadata

Data about data; describes characteristics and relationships.

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System Catalog

Stores metadata about the database.

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Information

Processed data that provides meaning and context.

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Good Information

Accurate, relevant, and timely.

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Ubiquitous Data

Data is abundant and everywhere.

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Pervasive Data

Data is widespread and persistent.

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Single-User Database

Supports one user at a time.

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Multiuser Database

Supports multiple users simultaneously.

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Workgroup Database

Supports a small group or department.

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Enterprise Database

Supports users across many departments.

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Centralized Database

Stores data at a single location.

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Distributed Database

Stores data across multiple locations.

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Cloud Database

Created and maintained using cloud data services.

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General-Purpose Database

Stores data across multiple subject areas.

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Discipline-Specific Database

Focuses on a particular subject area.

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Operational Database

Supports an organization's daily operations.

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Data Warehouse

Stores data optimized for analysis and decision-making.

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OLAP (Online Analytical Processing)

Analyzes and models data in a data warehouse.

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Business Intelligence (BI)

Processes business data to support decision-making.

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Structured Data

Data organized into a defined format.

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Unstructured Data

Data in its original raw state.

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Semistructured Data

Data that has been partially organized.

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NoSQL (Not Only SQL)

Handles large amounts of structured and unstructured data.

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DBMS

Software that manages database structure, controls access, and connects users to the database.

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Database System Components

Hardware, software, people, procedures, and data.

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Data Dictionary Management

Stores definitions of data elements and their relationships.

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Data Storage Management

Manages data storage and performance.

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Security Management

Protects data and controls user access.

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Multiuser Access Control

Allows simultaneous access without compromising data integrity.

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Backup and Recovery Management

Restores the database after a failure.

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Data Integrity Management

Minimizes redundancy and maximizes consistency.

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SQL

Structured Query Language; standard language for accessing and querying databases.

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DBMS Advantages

Improved data sharing, security, consistency, integration, and decision-making.

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DBMS Disadvantages

Higher costs, complexity, maintenance, vendor dependence, and upgrades.

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Database Design

Creating a flexible structure to store and manage data.

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Generative AI

Uses learned patterns from input data to generate new content.

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Machine Learning

Uses statistical techniques to identify patterns and predict trends.

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Natural Language Processing (NLP)

AI that processes human language.

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AI Challenges

Ethical concerns, data privacy, inherited bias, and resource utilization.