

Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
An overview of two common ways to organize data marts or entire data warehouses using relational databases: the star schema and the snowflake schema. Both schemas use dimension tables to describe data aggregated in a fact table. The sales model is used as an example to illustrate the differences between the two schemas.
Typology: Study notes
1 / 3
This page cannot be seen from the preview
Don't miss anything!


The star schema and the snowflake schema are ways to organize data marts or entire data warehouses using relational databases. Both of them use dimension tables to describe data aggregated in a fact table. Everyone sells something, be it knowledge, a product, or a service. Storing this information, either in an operational system or in a reporting system, is also a need. So we can expect to find some type of sales model inside the data warehouse of nearly every company. Let’s take one more look at the sales model in both the star and snowflake schemas.
The most obvious characteristic of the star schema is that dimension tables are not normalized. In the model above, the pink fact_sales table stores aggregated data created from our operational database(s). The light blue tables are dimension tables. We decided to use these five dimensions because we need to create reports using them as parameters. The granulation inside each dimension is also determined by our reporting needs. From this model, we can easily see why this schema is called the ‘star schema’: it looks like a star, with the dimension tables surrounding the central fact table.