Difference between Fact table and Dimension table?

When reading a book for business objects, I came across the term- fact table and dimension table.

I am trying to understand what is the different between Dimension table and Fact table?

I read couple of articles on the internet but I was not able to understand clearly..

Any simple example will help me to understand better?

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 associated facts 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.

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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.

Super simple explanation:

Fact table: a data table that maps lookup IDs together. Is usually one of the main tables central to your application.

Dimension table: a lookup table used to store values (such as city names or states) that are repeated frequently in the fact table.

Dimension table Dimension table is a table which contain attributes of measurements stored in fact tables. This table consists of hierarchies, categories and logic that can be used to traverse in nodes.

Fact table contains the measurement of business processes, and it contains foreign keys for the dimension tables.

Example – If the business process is manufacturing of bricks

Average number of bricks produced by one person/machine – measure of the business process

In the simplest form, I think a dimension table is something like a 'Master' table - that keeps a list of all 'items', so to say.

A fact table is a transaction table which describes all the transactions. In addition, aggregated (grouped) data like total sales by sales person, total sales by branch - such kinds of tables also might exist as independent fact tables.

1. The fact table mainly consists of business facts and foreign keys that refer to primary keys in the dimension tables. A dimension table consists mainly of descriptive attributes that are textual fields.
2. A dimension table contains a surrogate key, natural key, and a set of attributes. On the contrary, a fact table contains a foreign key, measurements, and degenerated dimensions.
3. Dimension tables provide descriptive or contextual information for the measurement of a fact table. On the other hand, fact tables provide the measurements of an enterprise.
4. When comparing the size of the two tables, a fact table is bigger than a dimensional table. In a comparison table, more dimensions are presented than the fact tables. In a fact table, less numbers of facts are observed.
5. The dimension table has to be loaded first. While loading the fact tables, one should have to look at the dimension table. This is because the fact table has measures, facts, and foreign keys that are the primary keys in the dimension table.

Read more: Dimension Table and Fact Table | Difference Between | Dimension Table vs Fact Table http://www.differencebetween.net/technology/hardware-technology/dimension-table-and-fact-table/#ixzz3SBp8kPzo

From my point of view,

• Dimension table : Master Data
• Fact table : Transactional Data

• a Fact = an action: a sale, a transaction, an access
• a Dimension = an object: a seller, a customer, a date, a price

Then...

• Facts references dimensions for: when, where, what, who, how

The real interesting thing is deciding whether an attribute should be a dimension or a fact. For example, the price of each item in an order, or, the maximum amount of a insurance recorded in a contract. There are no generally correct way to approach these, only ones that make sense in the context.

PS: If I were to create those jargons I would prefer Log table and Object table.

For Relation database users, Dimension is equivalent to Master Table. Fact is equivalent to Transaction table.

Dimension table : It is nothing but we can maintains information about the characterized date called as Dimension table.

Example : Time Dimension , Product Dimension.

Fact Table : It is nothing but we can maintains information about the metrics or precalculation data.

Example : Sales Fact, Order Fact.

Star schema : one fact table link with dimension table form as a Start Schema.

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