How do you compare and contrast star and snowflake schemas with other data warehouse modeling techniques?

1 Star schema

A star schema is a data warehouse modeling technique that consists of one central fact table and several dimension tables that are directly connected to it. The fact table contains the measures or metrics that you want to analyze, such as sales, revenue, or orders. The dimension tables contain the attributes or characteristics that describe the facts, such as product, customer, or location. The star schema is called so because it resembles a star with the fact table at the center and the dimension tables as the points.

2 Snowflake schema

A snowflake schema is a variation of the star schema that normalizes the dimension tables by splitting them into smaller tables. For example, instead of having one product table, you might have a product table, a category table, and a subcategory table, each with a foreign key that links to the next level. The snowflake schema is called so because it resembles a snowflake with the fact table at the center and the dimension tables as the branches.

3 Other techniques

Star and snowflake schemas are not the only data warehouse modeling techniques available; there are other approaches with distinct structures and purposes. For example, the fact constellation schema allows multiple fact tables to share some or all of the dimension tables, which is useful when analyzing different levels of granularity or perspectives of the same data. Additionally, the data vault schema is based on hubs, links, and satellites. Hubs store the business keys or identifiers of entities; links store the relationships between them; and satellites store their attributes or details. This schema is designed to handle changes and history of data over time, as well as enable parallel loading and querying.

4 Advantages and disadvantages

When selecting a data warehouse modeling technique, it is important to consider factors such as data quality, volume, and flexibility. Normalizing the data, as in the snowflake or data vault schema, can improve the data quality by reducing redundancy, inconsistency, and anomalies. However, it can also increase the complexity and number of joins, which can affect the query speed and readability. Denormalizing the data, as in the star or fact constellation schema, can reduce the data volume by eliminating unnecessary tables and columns. But this can also increase the data duplication and storage space, which can affect the loading time and scalability. On the other hand, having multiple fact tables or multiple levels of dimension tables can increase the data flexibility by allowing different views and analysis of the data. Nevertheless, this can also increase the data dependency and complexity, which can affect the data integrity and consistency.

5 Best practices

When selecting the best data warehouse modeling technique for your project, you should consider your specific data needs and trade-offs. To start, you must have a clear understanding of the questions you want to answer, the metrics you want to measure, and the dimensions you want to slice and dice. Additionally, it is important to find the right balance between detail and aggregation of your data. Too much detail can slow down queries and increase storage costs, while too much aggregation can limit insights and flexibility. It is also wise to follow basic principles of data modeling, such as avoiding unnecessary columns, indexes, and constraints, using appropriate data types and formats, and applying naming conventions and standards. Finally, regular tests and evaluations of your data warehouse should be conducted to check data quality, accuracy, and completeness; measure loading and querying speed; and compare results with expectations.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?