In this article, we will talk about the star schema data model. When should you use this to structure your data? What are its advantages over other data schemas? We’ll answer these questions and more in this article. What Is a Star Schema? The star schema is a popular approach for designing data warehousing and business intelligence data models. It uses dimensional modeling; in other words, it utilizes dimension tables with descriptive attributes that provide context for the measures in the fact table.

The process of defining your data warehousing system (DWH) has started. You’ve outlined the relevant dimension tables, which tie to the business requirements. These tables define what we weigh, observe and scale. Now we need to define how we measure. Fact tables are where we store these measurements. They hold business data that can be aggregated across dimension combinations. But the fact is that fact tables are not so easily described – they have flavors of their own.

In the previous two articles, we considered the two most common data warehouse models: the star schema and the snowflake schema. Today, we’ll examine the differences between these two schemas and we’ll explain when it’s better to use one or the other. 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.

In a previous article we discussed the star schema model. The snowflake schema is next to the star schema in terms of its importance in data warehouse modeling. It was developed out of the star schema, and it offers some advantages over its predecessor. But these advantages come at a cost. In this article, we’ll discuss when and how to use the snowflake schema. The Snowflake Schema if (typeof VertabeloEmbededObject === 'undefined') {var VertabeloEmbededObject = "

Today, reports and analytics are almost as important as core business. Reports can be built out of your live data; often this approach will do the trick for small- and medium-sized companies without lots of data. But when things get bigger – or the amount of data starts increasing dramatically – it’s time to think about separating your operational and reporting systems. Before we tackle basic data modeling, we need some background on the systems involved.