How can you apply dimensional data modeling principles?

2 Steps of dimensional data modeling

The process of dimensional data modeling involves four main steps: identifying the business process, defining the grain, choosing the dimensions, and selecting the facts. The business process is the main subject of analysis, such as sales, inventory, or customer service. The grain is the level of detail or granularity of each fact record, such as daily, monthly, or per transaction. The dimensions are the attributes that describe the facts, such as date, product, or customer. The facts are the numeric measures that quantify the business process, such as revenue, quantity, or cost.

3 Types of dimensional models

There are two common types of dimensional models: star schema and snowflake schema. A star schema consists of a central fact table that connects to multiple dimension tables through foreign keys. Each dimension table has a single primary key and a set of attributes. A snowflake schema is a variation of a star schema that normalizes some of the dimension tables into sub-dimension tables to reduce redundancy and save space. However, this also increases the complexity and the number of joins in queries.

4 Best practices of dimensional data modeling

To apply dimensional data modeling principles effectively, you should follow some best practices. This includes using descriptive and consistent names for tables, columns, and keys, as well as using surrogate keys instead of natural keys to link facts and dimensions. Additionally, it's important to avoid null values and use default values or flags instead. Conformed dimensions should be used to ensure consistency and compatibility across multiple fact tables. Degenerate dimensions can store attributes that are part of the fact table's primary key, such as order number or invoice number. Junk dimensions can group low-cardinality attributes that do not belong to any other dimension, such as flags, indicators, or codes. Bridge tables can handle many-to-many relationships between facts and dimensions, such as products and categories. Lastly, outrigger tables can store attributes that are rarely used or have a different grain than the main dimension table, such as zip code or country.

5 Examples of dimensional data modeling

To demonstrate how dimensional data modeling principles can be applied, here are some examples of star schemas for different business processes. For instance, a sales star schema may include a fact table with facts such as revenue, quantity, and profit, and dimension tables with dimensions such as date, product, customer, and store. An inventory star schema may contain a fact table with facts such as stock level, stock value, and stock movement, and dimension tables with dimensions such as date, product, location, and supplier. Additionally, a customer service star schema could have a fact table with facts such as number of calls, duration, and resolution, and dimension tables with dimensions such as date, customer, agent, and issue.