Difference between Fact table and Dimension table?
In Data Warehouse Modeling, a star schema and a snowflake schema consists of Fact and Dimension tables.
Fact Table:
- It contains all the primary keys of the dimension and associatedfacts or measures(is a property on which calculations can be made) like quantity sold, amount sold and average sales.
Dimension Tables:
- Dimension tables provides descriptive information for all the measurements recorded in fact table.
- Dimensions are relatively very small as comparison of fact table.
- Commonly used dimensions are people, products, place and time.
This appears to be a very simple answer on how to differentiate between fact and dimension tables!
It may help to think of dimensions as things or objects. A thing such as a product can exist without ever being involved in a business event. A dimension is your noun. It is something that can exist independent of a business event, such as a sale. Products, employees, equipment, are all things that exist. A dimension either does something, or has something done to it.
Employees sell, customers buy. Employees and customers are examples of dimensions, they do.
Products are sold, they are also dimensions as they have something done to them.
Facts, are the verb. An entry in a fact table marks a discrete event that happens to something from the dimension table. A product sale would be recorded in a fact table. The event of the sale would be noted by what product was sold, which employee sold it, and which customer bought it. Product, Employee, and Customer are all dimensions that describe the event, the sale.
In addition fact tables also typically have some kind of quantitative data. The quantity sold, the price per item, total price, and so on.
Source:http://arcanecode.com/2007/07/23/dimensions-versus-facts-in-data-warehousing/
This is to answer the part:
I was trying to understand whether dimension tables can be fact table as well or not?
The short answer (INMO) is No.That is because the 2 types of tables are created for different reasons. However, from a database design perspective, a dimension table could have a parent table as the case with the fact table which always has a dimension table (or more) as a parent. Also, fact tables may be aggregated, whereas Dimension tables are not aggregated. Another reason is that fact tables are not supposed to be updated in place whereas Dimension tables could be updated in place in some cases.
More details:
Fact and dimension tables appear in a what is commonly known as a Star Schema. A primary purpose of star schema is to simplify a complex normalized set of tables and consolidate data (possibly from different systems) into one database structure that can be queried in a very efficient way.
On its simplest form, it contains a fact table (Example: StoreSales) and a one or more dimension tables. Each Dimension entry has 0,1 or more fact tables associated with it (Example of dimension tables: Geography, Item, Supplier, Customer, Time, etc.). It would be valid also for the dimension to have a parent, in which case the model is of type "Snow Flake". However, designers attempt to avoid this kind of design since it causes more joins that slow performance. In the example of StoreSales, The Geography dimension could be composed of the columns (GeoID, ContenentName, CountryName, StateProvName, CityName, StartDate, EndDate)
In a Snow Flakes model, you could have 2 normalized tables for Geo information, namely: Content Table, Country Table.
You can find plenty of examples on Star Schema. Also, check this out to see an alternative view on the star schema model Inmon vs. Kimball. Kimbal has a good forum you may also want to check out here: Kimball Forum.
Edit: To answer comment about examples for 4NF:
- Example for a fact table violating 4NF:
Sales Fact (ID, BranchID, SalesPersonID, ItemID, Amount, TimeID)
- Example for a fact table not violating 4NF:
AggregatedSales (BranchID, TotalAmount)
Here the relation is in 4NF
The last example is rather uncommon.