fact tables and dimension tables serve distinct but complementary roles. Here’s a breakdown of their differences:
Fact Table:
Definition: A fact table contains quantitative data for analysis and is often denormalized.
Content: It holds measurable, numerical data (facts) such as sales amounts, transaction counts, or revenue.
Keys: Fact tables typically contain foreign keys that reference dimension tables and often have a composite primary key made up of these foreign keys.
Example: A sales fact table might include columns for order ID, product ID, customer ID, sales amount, and date.
Dimension Table:
Definition: A dimension table contains descriptive attributes (or fields) that provide context to the facts.
Content: It holds textual or categorical data that can be used to filter or group facts. This might include names, dates, locations, and other characteristics.
Keys: Dimension tables usually have a primary key that uniquely identifies each record, which is referenced by the fact table.
Example: A product dimension table might include columns for product ID, product name, category, and brand.

In data warehousing, fact tables and dimension tables serve distinct but highly complementary roles. Together, they support the structure of a star or snowflake schema, enabling efficient querying and reporting. Here’s a Snow Rider 3D breakdown of their primary differences:

Definition:
A fact table stores quantitative metrics related to business processes. These metrics, or facts, are typically the focus of analytical queries.

Contents:
Contains measurable, numeric data, such as:

Sales amount

Quantity sold

Revenue

Transaction count

Cost or profit

Keys:

Includes foreign keys that reference dimension tables.

May have a composite primary key formed by a combination of these foreign keys (e.g., Date_ID, Product_ID, Customer_ID).