Understanding Quotation Table vs. Fact Table in Snowflake Schema
In data warehousing, the snowflake schema is a popular method for organizing tables in a star or snowflake shape. The central table is the fact table, while the surrounding tables are dimension tables. However, there are cases where a quotation table is used instead of a fact table. This article will explore the differences between a quotation table and a fact table in a Snowflake schema.
What is a Fact Table?
A fact table is a central table in a Snowflake schema that stores quantitative data. It is usually located at the center of the schema and is connected to dimension tables. The fact table contains measures, which are numerical values that can be aggregated. Measures are typically added up, averaged, or otherwise manipulated to provide insights into the data.
Example of a Fact Table:
CREATE TABLE sales\_fact (
sale\_id INT,
product\_id INT,
quantity\_sold INT,
sale\_price DECIMAL(10,2),
total\_sale DECIMAL(10,2)
);
What is a Quotation Table?
A quotation table is a table that stores information about quotes or bids. It is similar to a fact table, but it does not contain measures. Instead, it contains information about the quote or bid, such as the quote date, the quote number, and the customer who requested the quote. A quotation table is often used in industries where quotes or bids are a common part of the business process, such as construction or manufacturing.
Example of a Quotation Table:
CREATE TABLE quotation\_table (
quotation\_id INT,
customer\_id INT,
quotation\_date DATE,
total\_quote DECIMAL(10,2)
);
Significance of Quotation Tables vs. Fact Tables
While a fact table and a quotation table may seem similar, there are some key differences between them. A fact table contains measures, which are numerical values that can be aggregated. A quotation table, on the other hand, does not contain measures. Instead, it contains information about quotes or bids. This information can be used to track the progress of a quote or bid, but it cannot be aggregated in the same way that measures can.
Another key difference between a fact table and a quotation table is the type of data they contain. A fact table typically contains data that is collected over time, such as sales data or production data. A quotation table, on the other hand, contains data that is collected at a specific point in time, such as when a quote is requested or when a bid is submitted.
Applications of Quotation Tables vs. Fact Tables
Quotation tables and fact tables have different applications in data warehousing. A fact table is typically used in data warehousing to provide insights into business performance. By aggregating measures in a fact table, businesses can track sales, production, and other key performance indicators.
A quotation table, on the other hand, is typically used in data warehousing to track the progress of quotes or bids. By storing information about quotes or bids in a quotation table, businesses can track the status of a quote or bid, the customer who requested the quote, and other important information.
In summary, a fact table and a quotation table are both important components of a Snowflake schema. While they may seem similar, there are key differences between them. A fact table contains measures, which are numerical values that can be aggregated. A quotation table, on the other hand, does not contain measures. Instead, it contains information about quotes or bids. By understanding the differences between a fact table and a quotation table, businesses can make better use of their data warehousing resources.
References
- Kimball, R., Ross, M., & Thornthwaite, L. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. John Wiley & Sons.
- Inmon, W. H. (2016). Building the Data Warehouse. John Wiley & Sons.
- Hernandez, M. (2018). Snowflake Schema vs Star Schema: Which One to Use? Retrieved from