What is a Junk Dimension in Datawarehousing
What is a Junk Dimension in Datawarehousing
我是彩笔 发表于3年前
What is a Junk Dimension in Datawarehousing
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摘要: 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 Datawarehouse - Junk DimensionYou 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.


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