Meta Description: Fact tables contain various metrics pertinent to business, and dimension tables contain descriptive attributes that help promote data-driven decision-making. If you have been researching data warehouses, you have probably stumbled across fact and dimension tables. Unfortunately, many resources talk about both while not explaining what they are or how they work.
If you have been researching data warehouses, you have probably stumbled across fact and dimension tables. Unfortunately, many resources talk about both while not explaining what they are or how they work.
So, if you have been wondering what the difference is. Read on to learn all about fact table vs dimension table.
By definition, fact tables and dimension tables are different.
Fact tables contain measurements, facts, and other varying metrics important to your business.
Dimension tables should be used in tandem with fact tables that hold critical information and descriptive attributes that are important for data-driven decision-making.
Fact tables and dimension tables, while they operate together, both hold a different purposes in the grand scheme of data warehousing. Databases like Snowflake depend on them to operate correctly.
The fact table is a focal point of the data warehousing model. It’s a foundational center with dimension tables. Its purpose is to provide dimensional data by measuring events. This, in turn, helps data analytics and reports.
Dimension tables serve to connect to fact tables. You imagine them as supporting elements that surround a foundational fact table. They serve to collect data that could be pertinent to operations.
The design of fact tables differs slightly from dimension tables.
Fact tables appear to be the center of the operation and are not very descriptive.
Dimension tables appear very wordy. They are this way in design because they need to represent data descriptively accurately. This ensures that data information is complete and consistently accurate.
The information that fact tables store is different from that of dimensional tables. They also store them in slightly differing ways.
Fact tables are designed to store labels and ensure that dimension tables get properly filtered domain values.
Dimension tables store information by loading specific atomic data into their dimensional structures.
Dimension tables are made up of multiple layers, while fact tables do not have any.
Fact tables layers or forms of hierarchy.
Dimension tables are entirely different in this area. Each portion of data could be layers of less important sub-data to go along with it. For example, customer location could have information like country, state, city, zip, P.O Box, and former addresses.
The way these two tables utilize primary keys is quite different.
The mapping of primary keys with fact tables goes from foreign keys to dimensions.
Dimensions tables differ in that primary keys are set in varying columns that effectively identify all dimensions.
The types of data contained within each of these tables are very different. Here is how.
Fact tables contain data that relates to certain types of dimensions. This could include data like sales, services, and dates in which they took place.
Dimension tables contain detailed subcategories of data. Take services, for example. Dimension tables will contain detailed information like the type of service, date, time, length of time it took, profit, etc.
8. There are Different Types
Under the category of these two tables, several different variations can be used for various purposes. Both of these tables are significantly different in this area.
Fact tables have three main types of facts: Additive, Semi-Additive, and Non-Additive
- Additive ensures that different attributes will be added to all dimensions
- Semi-Additive has some attributes added to dimensions while intentionally leaving others out.
- Non-Additive fact tables contain generic measurements of varying processes throughout a business.
There are many different types of dimension tables. The following is a list of what they are and how they work.
- Junk Dimensions are collections of varying attributes that do not belong to a particular group of dimensions.
- Outrigger Dimensions are a secondary form of dimensions that reference other dimension tables.
- Step Dimensions operate in a similar way to web search engines. Each step of the process will likely have a different row that carries on the operation.
- Role-Playing dimensions help fact tables when referenced many times. It allows each reference point to be linked to specific dimensions.
- Conformed Dimensions can be found among many different data marts and are primarily used to maintain consistency and efficiency.
- Swappable Dimensions come in handy when the same fact table is used regarding varying versions of the same dimension.
- Shrunken Rollup Dimensions are subdivided rows and columns that can be useful for developing multiple fact tables.
Fact tables and dimension tables are quite different. While fact tables contain facts and varying metrics pertinent to your business, dimension tables have descriptive attributes that help promote data-driven decision-making.