A star schema is a common way of organizing data in a data warehouse or a data mart. It is a type of dimensional modeling that consists of one or more fact tables and several dimension tables. The fact tables store the quantitative data or measures that are relevant for analysis, such as sales amount, order quantity, or profit margin. The dimension tables store the descriptive data or attributes that provide context for the facts, such as product name, customer name, or order date.
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In a star schema, each fact table is linked to one or more dimension tables by foreign keys. These foreign keys are the primary keys of the dimension tables, which uniquely identify each dimension record. A dimension record is a row in a dimension table that contains the values of the attributes for a specific entity, such as a product or a customer.
Each dimension record is related to thousands of fact records, meaning that each dimension value can appear in many rows of the fact table. For example, a product dimension record with the value “Laptop” can be associated with many fact records that have different sales amounts, order dates, or customer names. This relationship is called a one-to-many relationship, because one dimension record can have many corresponding fact records.
Why is this relationship important?
The one-to-many relationship between dimension and fact records is important for several reasons:
- It allows for efficient storage of data, because the dimension values are stored only once in the dimension table, and not repeated in every row of the fact table. This reduces the size of the data warehouse and improves its performance.
- It enables fast and flexible querying of data, because the dimension values can be used as filters, groupings, or aggregations in analytical queries. For example, one can easily find the total sales amount for a specific product, customer, or time period by joining the fact and dimension tables on their foreign keys.
- It supports business intelligence and decision making, because the dimension values provide meaningful and understandable labels for the facts. For example, one can easily interpret the results of a query that shows the sales amount by product name, rather than by product ID.
Conclusion
In a typical star schema, each dimension record is related to thousands of fact records. This means that each fact table is connected to one or more dimension tables by foreign keys, and each dimension value can appear in many rows of the fact table. This relationship is important for efficient storage, fast querying, and business intelligence of data in a data warehouse or a data mart.
