Definition of Terms
What is data modelling?
Data modelling is defined as the process of creating a visual representation of information to communicate connections between data points. Data modelling can explained simply as the process of designing how data is structured, stored, and related to each other so it can be used effectively for analysis to derive meaningful insights.
The importance of good data modelling
Data modeling is critical for creating a structured, accurate, and scalable blueprint for organizational data, ensuring high-quality, consistent, and efficient data management. It reduces development errors, eliminates data redundancy, speeds up application performance, and aligns data structures with business requirements, ultimately enabling better-informed decisions.
What is a Schema?
A schema in data science is the structural blueprint or design of a database, defining how data is organized, stored, and related. It specifies tables, column names, data types, and constraints or rules. It serves as a, "map" for data, ensuring integrity and enabling efficient querying and analysis.
Types of Schemas
Star Schema
Star schema is better for storing and analyzing large amounts of data. It has a fact table at its center and multiple dimension tables connected to it just like a star, where the fact table contains the numerical data that run business processes and the dimension table contains data related to dimensions such as product, time, people, etc. or we can say, this table contains the description of the fact table.
Snowflake Schema
Just like star schema, the snowflake schema also has a fact table at its center and multiple dimension tables connected to it, but the main difference in both models is that in snowflake schema – dimension tables are further normalized into multiple related tables. The snowflake schema is used for analyzing large amounts of data.
Wrap Up
A well-structured database schema maintains data integrity and supports scalability by providing a solid framework for dealing with growing data volumes and complexities.
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