Introduction

Fact tables are a fundamental component of data warehouse databases, central to storing and retrieving quantitative data for analysis. Here's an overview of their characteristics and roles:

Key Characteristics of Fact Tables:

Quantitative Data Storage:

Fact tables store measurable, quantitative data known as facts. Examples include sales revenue, the number of units purchased, transaction amounts, and hours worked.

High Volume of Records:

They typically contain many records as they accumulate transactional or event data over time.

Additive Nature:

The data, in fact, tables, are usually additive, meaning that they can be summed up across various dimensions. For instance, total sales revenue can be aggregated over time periods, geographical regions, or product categories.

Structure of Fact Tables:

Fact Columns:

These columns contain numeric data or facts, such as sales amount, order quantity, or cost. These metrics are analyzed and aggregated.

Foreign Keys:

Fact tables contain foreign keys that link to dimension tables. Each foreign key corresponds to a dimension and helps provide context to the fact data.

Example:

Consider a sales fact table in the Integro data warehouse. The table might have the following structure:

In this example:

• Date_ID, Product_ID, and Category_ID are foreign keys that link to corresponding dimension tables (Date, Product, and Category dimensions).
• Sales_Amount and Units_Sold are the measurable facts that are analyzed.

Role of Fact Tables in the Integro Data Warehouse:

Central Repository for Analysis

Fact tables serve as the primary source of data for analytical queries. They enable users to perform complex calculations and generate insights based on aggregated data.

Data Aggregation:

Fact tables support data aggregation across multiple dimensions, allowing the business to summarize data in various ways, such as total monthly sales, hours worked, cost of material, etc.

Historical Data Storage:

Fact tables maintain historical data, making tracking performance trends over time and conducting time-series analysis possible.

Summary

Fact tables are crucial for storing quantitative data in a data warehouse. They enable robust analysis and reporting by linking measurable facts with contextual dimensions. They are essential for businesses to gain insights, monitor performance, and make data-driven decisions.