Fact Table vs. Dimension Table - Differences Between The Two

What is a Fact Table?

In a data warehouse, a fact table is a table that stores the measurements, metrics, or facts related to a business operation. 

It is located at the center of a star or snowflake schema and is surrounded by dimension tables.

• When multiple fact tables are used, they can be organized using a "fact constellation schema." 
• A fact table has two types of columns: those that contain the facts and those that serve as foreign keys linking to dimension tables. 
• The primary key of a fact table is often a composite key made up of all of the foreign keys in the table. 
• Fact tables can hold various types of measurements, such as additive, non-additive, and partly additive measures, and store important information in the data warehouse. 
• They are useful for evaluating dimensional attributes because they provide additive values that can act as independent variables.

Let us look at some of the main characteristics of a Fact Table.

Characteristics of a Fact Table

The following are some of the characteristics of a fact table:

• Outrigger Dimensions: Outrigger dimensions are dimensions that refer to any other dimension table.
• Keys: Each fact table has a key that is made up of the primary keys from all of the dimension tables connected to that fact table. A concatenated key is one such key that specifically identifies the row of the fact table.
• Additive Measures: The fact table's attributes may be entirely, partially, or not at all additive. Measures that are applied to all dimensions are referred to as fully addition or additive measures. Quasi-measures are those that store the fundamental unit of measurement for any business process, whereas semi-additive measures add measures to some dimensions but not to all.
• Fact Table Grain: The level of detail or depth of the information recorded in a fact table is referred to as the table's grain. A successful fact table must be designed at the highest level.
• Degenerated Dimensions: A degenerated dimension is any dimension or attribute that is available in the fact table but cannot be added to or is non-additive.
• Sparse Data: Some records in the fact table have characteristics with null values or measurements, which means that the information they contain is not given or provided.
• Shrunken Rollup Dimensions: The dimensions that are created by subdividing the base dimension's columns and rows are known as shrunken rollup dimensions.

Understanding the Granularity of a Fact Table

The granularity of a fact table is a common way to describe it. The grain of a fact table refers to the most basic level at which the facts can be defined. For example, the grain of a sales fact table might be "sales volume by day by-product by the shop," meaning that each entry in the table is uniquely identified by a day, product, and shop. Other dimensions, such as location or region, may also be included in the fact table, but they do not contribute to the uniqueness of the entries. These "affiliate dimensions" can provide additional information to slice and dice, but they usually provide insights at a higher level of aggregation (since a region contains many stores).

We will now understand the dynamics of a Dimension Table.

What is a Dimension Table?

Dimension tables contain descriptions of the objects in a fact table and provide information about dimensions such as values, characteristics, and keys. 

• These tables are usually small, with a number of rows ranging from a few hundred to a few thousand. 
• The term "dimension table" refers to a set of data related to any quantifiable event and is the foundation for dimensional modeling. 
• Dimension tables have a column that serves as a primary key, allowing each dimension row or record to be uniquely identified. This key is used to link the dimension table to the fact tables. A surrogate key, which is a system-generated key, is often used to uniquely identify the rows in the dimension table.

To understand this better, let us look at some characteristics of a Dimension Table.

Characteristics of a Dimension Table

The following are some of the characteristics of a dimension table:

• Relationship Between Attributes: Although they are all included in the same dimension table, the attributes in it typically do not have a direct relationship with one another.
• Records: The dimension table contains more characteristics than records.
• Keys: The main key is required for every dimension table in order to help uniquely identify each entry.
• Normalization: The dimension table is not normalized because doing so splits the data into different tables and makes it more difficult for queries to execute quickly because they must go through these extra tables to retrieve measurements from the fact table for each corresponding attribute in the dimension table.
• Attributes: Because the dimension table has so many attributes, it looks to be expanding horizontally.
• Drilling Down, Rolling Up: The presence of attributes in a dimension table enables the extraction of information by drilling down from a higher level to a lower level or by rolling up from a lower level to a higher level of the attributes.
• Attribute Values: The majority of the values in the dimension table are expressed as text rather than numbers.

Now that we have a general understanding of fact and dimension tables, let us understand the key differences between these two types of tables in a data warehouse.

Fact Table vs. Dimension Table 

Despite being related to each other, the Fact and Dimension Tables consist of many differences. 

Here are some of their major differences:

To understand the underlying context of each of these concepts in a better way, we need to understand their various types. In the upcoming section, we will understand the types of facts and dimensions along with their detailed examples.

Types of Facts

The following are the different categories of facts:

Types of Dimensions

The major types of dimensions are categorized as follows:

After learning about Fact and Dimension Tables, their major differences, and their types, it's time to summarize the key differences between them to solidify our understanding of them.

Fact Table vs. Dimension Table: Key Differences

The key differences between Fact and Dimension Tables are as follows:

• The Dimension table is a partner to the fact table and contains descriptive qualities that can be used as query constraints. The fact table includes measurements, metrics, or facts about business operations.
• A star and snowflake schema's core is where the table containing it is located, while its edges are where the dimension table is situated.
• A fact table should be lengthy, descriptive, full, and of guaranteed quality, while a fact table is characterized by its grain or even most atomic level.
• While the Dimension database includes extensive information, the Fact table is used to hold report labels.
• In contrast to the Dimension table, which incorporates hierarchies, the Table does not.

To understand these key differences even better, let us go through an example that connects the relationship between Fact and Dimension tables.

Example of Fact Table vs. Dimension Table

Let's say a business sells goods to clients. Every transaction is a fact that actually occurs, and the fact table is where these facts are recorded. 

For instance:

We can now include a dimension table for customers:

The client ID column in the fact table is used to connect to the dimension table. For instance, if you look at row 2 of the fact table, you can see that client 3, Mia Riana, made a purchase of six items with order id 143. To find out more information about what Mia purchased and when the company would also have a product table and a timetable.

When designing fact tables, there are considerations related to both physical constraints and data limitations. It's important to think about the size of the table and the ways in which it will be accessed. Indexes can be helpful for optimizing both of these factors. However, from a logical design standpoint, there should be no limitations on the structure of the table. It should be designed based on current and future needs, with as much flexibility as possible to allow for future updates without needing to completely rebuild the data.

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FAQs

1. What is the dimension and fact table with examples?

The primary distinction between a fact table, also known as a reality table, and a dimension table is that the latter includes properties from which actual measurements are made.

2. What are facts and dimensions?

Data warehousing terminology includes facts and dimensions. A fact is a piece of information with a specific numerical value, like a sale or a download. Facts are kept in fact tables, which are linked to several dimension tables by a foreign key. Facts are accompanied by dimensions, which describe the items in a fact table.

3. Why is the fact table larger than the dimension table?

Fact tables have more records and fewer attributes, while dimension tables have more attributes and fewer records. While the dimension table expands horizontally, the fact table expands vertically. While the dimension table has a primary key, the table has a concatenated key.

4. What is the relationship between facts and dimensions?

A single-dimension membership can be connected to many facts in the majority of dimensions, where each fact links to one and only one member of the dimension. This is known as each relationship in relational database jargon. However, connecting a single piece of evidence to several dimension members is frequently helpful. 

5. What are the 5 types of dimensions?

There are 9 types of dimensions:

• Shrunken Rollup Dimensions
• Outrigger Dimensions
• Role-Playing Dimensions
• Step Dimensions
• Degenerate Dimensions
• Conformed Dimensions
• Junk Dimensions
• Dimension-to-Dimension Table Joins
• Swappable Dimensions

6. What are the three types of fact tables?

The three types of fact tables are as follows:

• Additive
• Non-Additive
• Semi-Additive