What are the most common design patterns for data warehouses?

1 Star Schema

One of the simplest and most widely used design patterns for data warehouses is the star schema. In this pattern, the data is organized into two types of tables: fact tables and dimension tables. Fact tables store the quantitative measures or metrics of the business, such as sales, revenue, or profit. Dimension tables store the descriptive attributes or dimensions of the business, such as product, customer, or location. Each fact table has a primary key that references one or more foreign keys from the dimension tables, forming a star-like structure. The star schema is easy to understand and implement, as it reduces the number of joins and maintains a high level of data granularity. However, it can also lead to data redundancy, data inconsistency, and large fact tables that affect the storage and query performance.

2 Snowflake Schema

Another common design pattern for data warehouses is the snowflake schema. In this pattern, the dimension tables are further normalized into multiple sub-dimension tables, each with its own primary key and foreign key relationships. This creates a snowflake-like structure, where the fact table is at the center and the dimension tables branch out into different levels of detail. The snowflake schema is more efficient in terms of storage and data quality, as it eliminates the data redundancy and inconsistency issues of the star schema. However, it also increases the complexity and the number of joins, which can affect the query performance and usability.

3 Data Vault

A more recent and advanced design pattern for data warehouses is the data vault. In this pattern, the data is organized into three types of tables: hub tables, link tables, and satellite tables. Hub tables store the unique identifiers or keys of the business entities, such as product, customer, or order. Link tables store the associations or relationships between the business entities, such as product-customer or order-customer. Satellite tables store the descriptive attributes or histories of the business entities, such as product name, customer address, or order date. Each table has a primary key that references one or more foreign keys from other tables, forming a vault-like structure. The data vault is more flexible and scalable than the star or snowflake schemas, as it can handle changes in the data sources, data structures, and business rules without affecting the existing data or queries. However, it also requires more effort and expertise to implement and maintain, as it involves more tables, more complex logic, and more metadata.

4 Hybrid Schema

A hybrid design pattern for data warehouses is one that combines the features and benefits of different patterns, such as star, snowflake, and data vault. For example, a hybrid schema can use a data vault to store the raw and historical data from various sources, and then use a star or snowflake schema to create derived and aggregated data for specific analytical purposes. This way, the hybrid schema can achieve a balance between the storage efficiency, data quality, query performance, and usability of the data warehouse. However, a hybrid schema can also introduce more challenges and trade-offs, such as data duplication, data synchronization, and data governance.

5 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?