DATA WAREHOUSING CONCEPTS

Types of Facts

There are three types of facts:

• Additive: Additive facts are facts that can be summed up through all of the dimensions in the fact table.
• Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others.
• Non-Additive: Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table.

Let us use examples to illustrate each of the three types of facts. The first example assumes that we are a retailer, and we have a fact table with the following columns:

Date

Store

Product

Sales_Amount

The purpose of this table is to record the sales amount for each product in each store on a daily basis. Sales_Amount is the fact. In this case, Sales_Amount is an additive fact, because you can sum up this fact along any of the three dimensions present in the fact table -- date, store, and product. For example, the sum of Sales_Amount for all 7 days in a week represent the total sales amount for that week.
Say we are a bank with the following fact table:

Date

Account

Current_Balance

Profit_Margin

The purpose of this table is to record the current balance for each account at the end of each day, as well as the profit margin for each account for each day. Current_Balance and Profit_Margin are the facts. Current_Balance is a semi-additive fact, as it makes sense to add them up for all accounts (what's the total current balance for all accounts in the bank?), but it does not make sense to add them up through time (adding up all current balances for a given account for each day of the month does not give us any useful information). Profit_Margin is a non-additive fact, for it does not make sense to add them up for the account level or the day level.

Types of Fact Tables

Based on the above classifications, there are two types of fact tables:

• Cumulative: This type of fact table describes what has happened over a period of time. For example, this fact table may describe the total sales by product by store by day. The facts for this type of fact tables are mostly additive facts. The first example presented here is a cumulative fact table.
• Snapshot: This type of fact table describes the state of things in a particular instance of time, and usually includes more semi-additive and non-additive facts. The second example presented here is a snapshot fact table. 

What is a Junk Dimension in Datawarehousing

The junk dimension is simply a structure that provides a convenient place to store the junk attributes. It is just a collection of random transactional codes, flags and/or text attributes that are unrelated to any particular dimension.

In OLTP tables that are full of flag fields and yes/no attributes, many of which are used for operational support and have no documentation except for the column names and the memory banks of the person who created them. Not only do those types of attributes not integrate easily into conventional dimensions such as Customer, Vendor, Time, Location, and Product, but you also don’t want to carry bad design into the data warehouse.However, some of the miscellaneous attributes will contain data that has significant business value, so you have to do something with them.

This scenario is especially common in legacy systems and databases that were created without solid, underlying design principles. Column names such as Completed, Packed, Shipped, Received, Delivered, and Returned (each with yes/no data values) are very common, and they do have business value.These miscellaneous indicators and flags that don't logically belong to the core dimension tables.  They are either too valuable to ignore or exclude.Often the meaning of the flags and text attributes is obscure. This situation leaves the designer with a number of bad alternatives

Designers sometimes want to treat them as Fact or make it into numerous small Dimensional tables. However, all of these options are less than ideal. Discarding the data can be dangerous because the miscellaneous values, flags, and yes/no fields might contain valuable business data. Including the miscellaneous attributes in the fact table could cause the fact table to swell to alarming proportions, especially if you have more than just a few miscellaneous attributes. The increased size of the fact table could cause serious performance problems because of the reduced number of records per physical I/O. Even if you tried to index these fields to minimize the performance problems, you still wouldn’t gain anything because so many of the miscellaneous fields contain flag values such as 0 and 1; Y and N; or open, pending, and closed.

A third, less obvious but preferable, solution is to incorporate a Junk Dimension as a holding place for these flags and indicators.

Advantage of junk dimension:

• It provides a recognizable location for related codes, indicators and their descriptors in a dimensional framework.
• This avoids the creation of multiple dimension tables.
• Provide a smaller, quicker point of entry for queries compared to performance when these attributes are directly in the fact table. 
• An interesting use for a junk dimension is to capture the context of a specific transaction.  While our common, conformed dimensions contain the key dimensional attributes of interest, there are likely attributes about the transaction that are not known until the transaction is processed.

Above figure shows a junk dimension. As in any dimensional design, each of the rows in the fact table will be associated with a row in this junk dimension.

Simple Data warehouse - Junk Dimension

You want to keep the data warehouse design as simple and straightforward as possible, so that users will be able to access data easily. Miscellaneous attributes that contain business value are a challenge to include in your data warehouse design because they don’t fit neatly into conventional dimensions, and if improperly handled, can cause the data warehouse to swell in size and perform suboptimally. By placing miscellaneous attributes into junk dimensions, you can circumvent both of these problems.