In the field of data science and analytics, there are two main types of tables: dimension tables and fact tables. Both are essential for a successful data management strategy, but they serve different purposes. In this post, we’ll explore the key differences between these two table types. We’ll also look at some examples to help illustrate how they’re used in practice. By understanding the difference between dimension tables and fact tables, you’ll be better equipped to choose the right type of table for your needs.
What is Dimension Table?
Dimension tables are used in data warehouses to store information about the data that is being tracked. The dimension table contains the attributes of the data, such as the date, time, product, and location. The fact table contains the measures for each of the attributes in the dimension table. For example, a sales fact table might contain a measure for the quantity sold, while a Dimension Table might contain the product name and description. Dimension tables are often used to provide context for the measures in a fact table. For example, a Dimension Table might be used to provide information about the products that were sold in a particular transaction. Dimension tables can also be used to track changes over time, such as price changes or inventory levels. Dimension tables are an essential part of any data warehouse and can be used to provide valuable insights into the data that is being tracked.
What is a Fact Table?
A Fact Table is a table in a star schema of a data warehouse. Fact tables store quantitative information for analysis and are often denormalized. A Fact Table consists of facts and dimensions. Facts are the numeric measurements that can be analyzed. Dimensions are the descriptors of the facts such as time, product, geography, channel, and so on. There can be multiple Fact Tables in a star schema but each Fact Table is usually related to a single central theme. For example, in a data warehouse focused on retail sales, there might be separate Fact Tables for store sales, online sales, and catalog sales.
Linking these Fact Tables together would allow an analysis of how different channels contribute to overall sales. Fact Tables typically contain a large number of columns and only a few rows. The primary key of a Fact Table is usually a composite key made up of the foreign keys from the associated Dimension Tables. A Fact Table typically has two types of columns: those that contain the factual data being analyzed, and those that are foreign keys to the associated Dimension Tables. Fact Tables are sometimes called Measurement Tables or Detail Tables.
Difference between Dimension Table and Fact Table
Dimension tables and fact tables are the two main types of tables in a data warehouse. Dimension tables store information about the dimensions of a business, such as time, product, price, and location. Fact tables store facts, or measures, about a business, such as sales and inventory levels. Dimension tables are often used to slice and dice data in order to answer specific business questions. For example, a retailer might use a dimension table to find out which products are selling well in which locations. Fact tables, on the other hand, are typically used to support aggregate calculations, such as total sales or total inventory. In short, dimension tables provide the details while fact tables provide the big picture.
The main difference between dimension tables and fact tables is that dimensions are static, meaning they don’t change often, while facts are dynamic and can be updated frequently. Dimension tables contain descriptive information about your data, such as customer name, address, or product SKU. Fact tables contain the numerical measures associated with your data, such as sales totals or inventory levels. When designing your database schema, you’ll need to decide which table will hold each type of data. While there isn’t necessarily a right or wrong answer, understanding the differences between these two types of tables will help you make the most informed decision for your business.