Star schema and Snowflake schema
Star schema and Snowflake schema are two common data modeling techniques used in data warehousing and business intelligence. Both of these schemas are used to organize and structure data in a way that makes it easy to query and analyze.
A Star schema is a type of data modeling technique where a central fact table is connected to one or more dimension tables through foreign key relationships. The fact table contains the measures or facts that are being analyzed, while the dimension tables contain the attributes or characteristics of the facts. This schema is called a "star" schema because the diagram of the schema looks like a star, with the fact table at the center and the dimension tables radiating outwards.
For example, in a retail company, a sales fact table may be connected to a product dimension table, a customer dimension table, and a time dimension table. The sales fact table would contain measures such as sales amount and quantity sold, while the dimension tables would contain attributes such as product name, customer demographics, and date of sale. This type of schema allows for easy aggregation and analysis of sales data by product, customer, and time.
On the other hand, Snowflake schema is a variant of the star schema in which the dimension tables are normalized, meaning that they are split into multiple related tables. This is done to reduce data redundancy and increase the flexibility of the schema. In Snowflake schema, the dimension tables are connected to the fact table through multiple levels of relationships. The diagram of the schema looks like a snowflake, with the fact table at the center and the dimension tables branching outwards like the arms of a snowflake.
For example, in a retail company, a sales fact table may be connected to a product dimension table, which is connected to a product category dimension table and a supplier dimension table. The sales fact table would contain measures such as sales amount and quantity sold, while the dimension tables would contain attributes such as product name, product category, and supplier name. This type of schema allows for a more detailed and flexible analysis of sales data by product, product category, and supplier.
In summary, both Star schema and Snowflake schema are used to organize and structure data for easy querying and analysis. Star schema is a simple and easy-to-understand schema that is best suited for situations where data redundancy is not a concern. Snowflake schema is a more complex schema that is best suited for situations where data redundancy is a concern, and the data needs to be normalized for more detailed and flexible analysis.
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