Data Modeling and Normalization

Data Modeling

• Data modeling is the process of creating a conceptual representation of data structures and relationships.
• It involves identifying entities, attributes, and relationships between them.
• Goal: to create a logical and consistent model of the data.

Normalization

• Normalization is the process of organizing data in a database to minimize data redundancy and dependency.
• It involves dividing large tables into smaller, related tables to improve data integrity and reduce data anomalies.
• Normalization rules:
  • 1NF: each table cell contains a single value.
  • 2NF: each non-key attribute depends on the entire primary key.
  • 3NF: if a table is in 2NF, and a non-key attribute depends on another non-key attribute, then it should be moved to a separate table.

Database Design

• Database design is the process of creating a detailed, logical representation of the database.
• It involves defining the schema, including the relationships between tables and the constraints on the data.
• Steps:
  1. Define the database requirements.
  2. Identify the entities and relationships.
  3. Define the schema.
  4. Normalize the data.
  5. Define the physical database design.

Data Integrity

• Data integrity refers to the accuracy, completeness, and consistency of data.
• Types of data integrity:
  • Entity integrity: each row in a table has a unique identifier.
  • Referential integrity: relationships between tables are maintained.
  • Domain integrity: data values are within specified ranges or formats.
  • User-defined integrity: custom rules and constraints defined by the user.

SQL Queries

• SQL (Structured Query Language) is a language used to manage and manipulate data in relational databases.
• Types of SQL queries:
  • DDL (Data Definition Language): creates, modifies, and deletes database structures.
  • DML (Data Manipulation Language): inserts, updates, and deletes data.
  • DQL (Data Query Language): retrieves data from the database.

3-Level Schema

• The 3-level schema is a conceptual architecture for a database management system.
• Levels:
  1. Internal level: physical storage of data on disk.
  2. Conceptual level: logical representation of the data, including the schema and relationships.
  3. External level: user's view of the data, including the interface and queries.

Note: These notes provide a concise overview of the key concepts and topics related to databases.

Data Modeling

• Data modeling is the process of creating a conceptual representation of data structures and relationships to identify entities, attributes, and relationships between them.
• The goal of data modeling is to create a logical and consistent model of the data.

Normalization

• Normalization is the process of organizing data in a database to minimize data redundancy and dependency.
• It involves dividing large tables into smaller, related tables to improve data integrity and reduce data anomalies.
• Normalization rules include:
  • 1NF: each table cell contains a single value.
  • 2NF: each non-key attribute depends on the entire primary key.
  • 3NF: if a table is in 2NF, and a non-key attribute depends on another non-key attribute, then it should be moved to a separate table.

Database Design

• Database design is the process of creating a detailed, logical representation of the database.
• It involves defining the schema, including the relationships between tables and the constraints on the data.
• Steps in database design include:
  • Defining the database requirements.
  • Identifying the entities and relationships.
  • Defining the schema.
  • Normalizing the data.
  • Defining the physical database design.

Data Integrity

• Data integrity refers to the accuracy, completeness, and consistency of data.
• Types of data integrity include:
  • Entity integrity: each row in a table has a unique identifier.
  • Referential integrity: relationships between tables are maintained.
  • Domain integrity: data values are within specified ranges or formats.
  • User-defined integrity: custom rules and constraints defined by the user.

SQL Queries

• SQL (Structured Query Language) is a language used to manage and manipulate data in relational databases.
• Types of SQL queries include:
  • DDL (Data Definition Language): creates, modifies, and deletes database structures.
  • DML (Data Manipulation Language): inserts, updates, and deletes data.
  • DQL (Data Query Language): retrieves data from the database.

3-Level Schema

• The 3-level schema is a conceptual architecture for a database management system.
• The three levels of the 3-level schema are:
  • Internal level: physical storage of data on disk.
  • Conceptual level: logical representation of the data, including the schema and relationships.
  • External level: user's view of the data, including the interface and queries.

Normalization

• Organizes data to minimize data redundancy and dependency
• First Normal Form (1NF): each table cell must contain a single value
• Second Normal Form (2NF): each non-key attribute depends on the entire primary key
• Third Normal Form (3NF): non-key attribute depends on another non-key attribute, then it's moved to a separate table

Database Design

• Defines the database structure and relationships
• Separation of Concerns: separates data into different tables based on functions
• Minimizes Data Redundancy: eliminates duplicate data to reduce errors and inconsistencies
• Data Normalization: organizes data to minimize data redundancy and dependency
• Data Integrity: ensures data accuracy and consistency

Data Integrity

• Ensures data accuracy, completeness, and consistency
• Entity Integrity: ensures each row in a table has a unique identifier
• Referential Integrity: ensures relationships between tables are consistent
• Domain Integrity: ensures data values are valid and consistent
• User-Defined Integrity: custom rules and constraints defined by the user

SQL Queries

• Manages and manipulates relational databases
• DML (Data Manipulation Language): performs operations like INSERT, UPDATE, and DELETE
• DDL (Data Definition Language): creates, modifies, and deletes database structures like tables and indexes
• TCL (Transaction Control Language): manages transactions and locking mechanisms
• DQL (Data Query Language): retrieves data from the database

Data Modeling

• Creates a conceptual representation of data structures and relationships
• Entity-Relationship Modeling (ERM): represents data as entities, attributes, and relationships
• Object-Oriented Modeling: represents data as objects and their interactions
• Dimensional Modeling: represents data as facts and dimensions

3-Level Schema

• Organizes data into three levels: Internal, Conceptual, and External
• Internal Level: physical storage and access methods
• Conceptual Level: logical structure and relationships
• External Level: user views and interfaces
• Provides a separation of concerns and improves data flexibility and scalability