Understanding Fact and Dimension Tables in Star and Snowflake Schemas

Facts and Dimensions Tables are used in data warehousing and business intelligence to organize and analyze data.Facts Table     •  Facts are like the main events or things we  want to keep track of (e.g., number of toys, number of kids).- Contains the main data points or measurements (facts) that we want to analyze- Typically has a few columns (fields) that describe the fact, such as:    - Unique identifier (ID)    - Date or time    - Value or amount    - Category or type- Examples of facts tables:    - Sales: stores sales data (amount, date, region, product)    - Customer: stores customer data (name, address, phone, email)Dimensions Table:     • Dimensions are like the characteristics or features of those facts  (e.g., toy type, location, time).- Contains descriptive attributes or characteristics (dimensions) of the facts- Provides context and meaning to the facts- Typically has multiple columns (fields) that describe the dimension, such as:    - Category (e.g., product, region, time)    - Attribute (e.g., product name, region name, date)    - Description (e.g., product description, region description)- Examples of dimensions tables:    - Product: stores product information (name, description, category)    - Region: stores region information (name, description, country)Relationship between Facts and Dimensions- Facts tables are connected to Dimensions tables through foreign keys- Each fact is related to one or more dimensions (e.g., a sales fact is related to a product dimension and a region dimension)- This connection allows for analysis and querying of facts by dimension attribute.Snowflake Schema and Star SchemaWhen we have a lot of data, we need a way to store and connect it all. That's where Snowflake and Star Schemas come in!Snowflake Schema- This is like a big, intricate snowflake with many connected points.- It's a detailed and organized way to store data, with many tables connected to each other.- Good for: Complex data, lots of relationships between dataStar Schema:- This is like a simple star with a central point and straight lines connecting to it.- It's a simpler way to store data, with one main table and a few connected tables.- Good for: Simple data, fast querying and reportingIn short, Snowflake Schema is like a detailed, connected web, while Star Schema is like a simple, centralized hub.