The fact table is a fundamental component of a dimensional data model in data warehousing. It stores quantitative data, known as facts, about a business process or activity. Here are the key characteristics of a fact table:

 

1. Numeric Measures:

   • A fact table primarily contains numeric measures or metrics that represent the quantitative aspects of a business process. These measures could include sales revenue, quantity sold, cost, profit margin, units produced, or any other relevant numerical data.

2. Granularity:

   • Fact tables are designed to capture data at a specific level of granularity or detail. The granularity determines the level at which facts are aggregated and analyzed. For example, a fact table may store sales data at the daily, weekly, monthly, or quarterly level.

3. Foreign Keys:

   • Fact tables include foreign keys that establish relationships with dimension tables. These foreign keys serve as references to the primary keys in dimension tables and enable multidimensional analysis by linking facts with descriptive attributes stored in dimensions.

4. Additive Measures:

   • Facts in a fact table are often additive, meaning they can be aggregated or summed up across different dimensions. For example, sales revenue can be aggregated across products, regions, or time periods to derive total sales figures.

5. Non-Additive Measures:

   • In addition to additive measures, fact tables may also include non-additive measures such as averages, ratios, or percentages. These measures may require special handling during aggregation to ensure accurate analysis.

6. Surrogate Keys:

   • Fact tables often include surrogate keys, which are artificial keys generated to uniquely identify each row in the table. Surrogate keys are used in place of natural keys for efficiency and consistency in data warehousing environments.

7. Types of Fact Tables:

   • There are different types of fact tables based on the nature of the business process they represent, including:
     • Transactional Fact Tables: Capture individual business transactions or events, such as sales orders, purchases, or shipments.
     • Periodic Snapshot Fact Tables: Store data captured at regular intervals, such as daily, weekly, or monthly summaries of business activities.
     • Accumulating Snapshot Fact Tables: Track the progress or lifecycle of a business process over time, typically by recording key milestones or events.

8. Sparse Data:

   • Fact tables often contain sparse data, meaning not all combinations of dimensions may have associated facts. This is because not all business events or transactions occur for every possible combination of dimension values.

9. Normalized Structure:

   • Fact tables are typically denormalized structures optimized for query performance and analytical processing. However, they maintain referential integrity with dimension tables through foreign key relationships.

10. High Volume:

    • Fact tables often contain a large volume of data, especially in enterprise data warehousing environments where they accumulate historical data over time.

 

Overall, a fact table serves as the core repository for storing quantitative data related to business processes in a data warehousing environment. It enables multidimensional analysis and reporting by linking facts with descriptive attributes stored in dimension tables.

 

 

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