How can you reduce data redundancy in a dimensional model?

1 What is a dimensional model?

A dimensional model is a data structure that organizes data into two types of tables: fact tables and dimension tables. Fact tables contain the numerical measures or facts that are relevant for analysis, such as sales, revenue, or profit. Dimension tables contain the descriptive attributes or dimensions that define the context of the facts, such as product, customer, or time. A dimensional model is often represented as a star schema or a snowflake schema, where each fact table is linked to one or more dimension tables by foreign keys.

2 Why does data redundancy occur in a dimensional model?

Data redundancy can occur in a dimensional model for various reasons, such as having multiple fact tables with the same dimensions, multiple levels of granularity or aggregation in the fact tables, or multiple attributes or hierarchies in the dimension tables. This can lead to duplicated dimension data across tables, repeated facts at different levels, and overlapping or redundant information.

4 How can you reduce data redundancy in the dimension tables?

One way to reduce data redundancy in the dimension tables is to use a normalized dimension approach. This means that you split a dimension table into multiple sub-dimension tables, based on the logical relationships and hierarchies among the attributes, avoiding the repetition of information. For example, if you have a customer dimension table that has attributes such as name, address, city, state, and country, you can split it into a customer sub-dimension table, an address sub-dimension table, and a location sub-dimension table.

Another way to reduce data redundancy in the dimension tables is to use a surrogate key approach. This means that you assign a unique and sequential identifier to each row in the dimension table, avoiding the dependence on the natural or business keys that may change over time. For example, if you have a product dimension table that has a product code as the natural key, you can use a surrogate key instead, as the product code may change due to updates or mergers.

5 How can you balance data redundancy and performance in a dimensional model?

Reducing data redundancy in a dimensional model can have benefits such as saving storage space, improving data quality, and simplifying data maintenance. However, it can also add complexity to the model, increase the number of joins and queries, and affect performance and speed. Thus, you must carefully weigh the pros and cons of data redundancy versus performance, depending on your business needs and objectives. Factors that can help you decide how much data redundancy to tolerate or eliminate include the frequency and volume of data updates and changes, the size and diversity of data sources and users, the type and level of analysis and reporting required, as well as the availability and cost of hardware and software resources.

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?