To begin, let's review relational databases. They consist of tables connected through primary and foreign keys, which establish relationships between the tables. For example, in a car dealership database, the Branch ID serves as the primary key in the car dealerships table, while acting as a foreign key in the product details table. This establishes a direct connection between these two tables. Additionally, the VIN acts as the primary key in the product details table and as a foreign key in the repair parts table. These connections create relationships among all the tables, even connecting the car dealerships and repair parts tables through the product details table.
In traditional relational databases, a primary key ensures uniqueness in a specific column, whereas in BI, a primary key ensures uniqueness for each record in a table. In the car dealership database example, Branch ID, VIN, and part ID act as primary keys in their respective tables .
Now, let's delve into dimensional modeling. Dimensional models are a type of relational model specifically optimized for efficient data retrieval from data warehouses. They consist of two main components: facts and dimensions.
Facts represent measurements or metrics, such as monthly sales numbers, and dimensions provide additional context and detail about those facts. Dimensions answer the questions of who, what, where, when, why, and how. Using the example of monthly sales numbers, dimensions could include information about customers, store locations, and the products sold.
In dimensional modeling, attributes play a crucial role. Attributes describe the characteristics or qualities of data and are used to label table columns. In the car dealership example, attributes for the customer dimension might include name, address, and phone number for each customer.
To implement dimensional models, two types of tables are created: fact tables and dimension tables. Fact tables contain measurements or metrics related to specific events. Each row in the fact table represents one event, and the table can aggregate multiple events, such as daily sales. On the other hand, dimension tables store attributes of dimensions related to the facts. These tables are joined with the appropriate fact table using foreign keys, providing meaning and context to the facts.
By understanding how dimensional modeling builds connections between tables, BI professionals gain insights into effective database design. Dimensional models simplify data analysis by organizing data in a way that facilitates efficient retrieval and provides meaningful context. This knowledge also clarifies database schemas, which are the output of design patterns.
In summary, dimensional modeling is a powerful technique used in business intelligence to optimize data retrieval from data warehouses. It leverages facts, dimensions, and attributes to create meaningful connections between tables. Fact tables contain measurements, while dimension tables store attributes related to those measurements. This modeling approach simplifies data analysis and enhances database design for BI professionals
