Data Modeling 101

What is Data Modeling?

• A process of defining and organizing data requirements for a system.
• Involves creating a conceptual representation of data structure within a database.
• Supports business needs and processes.

Levels of Data Abstraction

• Conceptual Data Model: High-level view of data focusing on business concepts and rules, not technical details.
• Logical Data Model: Defines the data entities and their relationships, as well as data types and constraints.
• Physical Data Model: Specifies how the database is physically implemented, including storage structures, indexes, and access methods.

Keys in DBMS

• Attributes used to identify specific rows in a table and find relationships between tables.
• Primary Key (PK): Uniquely identifies each row in a table.
• Foreign Key (FK): Links rows in one table to rows in another table.

Relationship Notation

• In crow's foot notation, multiplicity refers to the maximum number of times an instance of one entity can be associated with instances of the related entity.
• Minimum number of times is represented by zero or one (optional or mandatory).

Data Modeling Types

• Relational: Organizes data into tables with rows and columns.
• Dimensional: Used for data warehousing, with fact tables and dimension tables.
• Entity-Relationship (E-R): Represents entities and their relationships using diagrams.

Benefits of Data Modeling

• Improves communication between stakeholders.
• Enhances data integrity and consistency.
• Provides a blueprint for database implementation.
• Aids in data analysis and decision-making.

Data Modeling Tools

• Lucidchart: Free online tool with built-in ER diagram function.

Limitations of Data Modeling

• Can be complex and time-consuming to create.
• May not capture all business requirements.
• Requires expertise in data modeling techniques.