How do you compare snowflake schema with other OLAP models and architectures?

1 What is snowflake schema?

Snowflake schema is a type of dimensional modeling that normalizes the dimension tables in a star schema. A star schema consists of a central fact table that contains the measures or metrics of interest, and several dimension tables that describe the attributes or dimensions of the facts. For example, a sales fact table may have dimensions such as product, customer, time, and location. Snowflake schema splits some of the dimension tables into multiple tables based on their hierarchical relationships, creating a snowflake-like structure. For example, the product dimension table may be divided into product category, subcategory, and product tables.

2 Why use snowflake schema?

Snowflake schema has some advantages over star schema, such as reducing data redundancy and storage space, improving data quality and integrity, and allowing for more detailed analysis. However, it also has some drawbacks, such as increasing the complexity of queries, reducing the flexibility and scalability of the data model, and potentially being incompatible with some OLAP tools or applications.

3 How to compare snowflake schema with other OLAP models and architectures?

Snowflake schema is not the only option for OLAP systems. Depending on data characteristics, analytical requirements, and technical constraints, other alternatives may be considered. Fact constellation is an extension of star schema that allows multiple fact tables to share dimension tables, useful when different levels of granularity or perspectives are needed. Multidimensional OLAP (MOLAP) stores data in a multidimensional array or cube, enabling faster and more efficient access, but requires more preprocessing and storage space. Hybrid OLAP (HOLAP) combines the benefits of MOLAP and relational OLAP (ROLAP), but introduces complexity and overhead in managing and synchronizing the data.

4 How to choose the best OLAP model and architecture for your data?

When determining the best OLAP model and architecture for your data, there is no one-size-fits-all answer. You need to consider factors such as the size, complexity, and diversity of your data set, the frequency, type, and scope of your analytical queries, the performance, scalability, and reliability of your OLAP system, and the compatibility, functionality, and usability of your OLAP tools or applications. It may be necessary to test and compare different options in order to find the optimal solution for your specific scenario. In some cases, a combination of different models and architectures may be required to achieve the desired results.

5 What are some best practices for designing and implementing snowflake schema?

If you decide to use snowflake schema for your OLAP system, there are several tips to help you design and implement it effectively. Identify the key facts and dimensions of your data and their relationships, and normalize the dimension tables only when necessary to avoid data redundancy and improve data quality. Utilize surrogate keys to link the fact and dimension tables, as well as to handle changes in the dimension values. Additionally, use descriptive names and labels for the tables and columns to enhance readability and understanding. Optimize the queries and indexes to reduce the number of joins and improve performance. Finally, document and communicate the data model and its logic to the users and stakeholders.

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?