How do you select the right data warehouse model?

1 Dimensional Model

The dimensional model is the most common and popular data warehouse model. It consists of two types of tables: fact tables and dimension tables. Fact tables store the quantitative data or measures, such as sales, revenue, or transactions. Dimension tables store the descriptive data or attributes, such as customer, product, or date. The fact tables and dimension tables are linked by foreign keys, forming a star or snowflake schema. The dimensional model is easy to understand, query, and optimize. It supports fast and flexible analysis of data from different perspectives or dimensions. However, the dimensional model can also have some drawbacks, such as data redundancy, complex ETL processes, and difficulty in handling changes in business requirements.

2 Normalized Model

The normalized model is the opposite of the dimensional model. It follows the principles of database normalization, which aim to reduce data redundancy, inconsistency, and anomalies by dividing the data into multiple tables with minimal dependencies and relationships. The normalized model is suitable for transactional systems, where data integrity and consistency are paramount. However, the normalized model is not ideal for data warehousing, where analytical queries and reporting are the main goals. The normalized model can result in poor performance, complex queries, and low user-friendliness.

3 Data Vault Model

The data vault model is a hybrid data warehouse model that combines the best features of the dimensional and normalized models. It consists of 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 customer or product. Link tables store the associations or relationships between the business entities, such as customer-product or product-order. Satellite tables store the descriptive or historical data of the business entities or their relationships, such as customer name, product price, or order date. The data vault model is designed to handle large volumes of data, enable parallel loading, preserve data history, and accommodate changes in business requirements. However, the data vault model can also be complex, expensive, and challenging to implement and maintain.

4 How to Select

When selecting the right data warehouse model, there is no one-size-fits-all solution. The best choice depends on various factors, such as your business objectives, data sources, volume, quality, users, query patterns, and budget. Generally speaking, the dimensional model is ideal for a simple and efficient data warehouse that supports fast and flexible analysis from multiple dimensions. The normalized model is suitable for a consistent and reliable data warehouse that preserves integrity and minimizes redundancy. The data vault model is excellent for a scalable and historical data warehouse that can handle large and complex sets of data. Combining models can also be beneficial in order to leverage the strengths of each model while overcoming their limitations; for instance, using the data vault model as the core data warehouse with the dimensional model as the presentation layer.

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?