Data Granularity

Data Granularity

In an operational system, data is usually kept at the lowest level of detail. In a point-of-sale system for a grocery store, the units of sale are captured and stored at the level of units of a product per transaction at the checkout counter. In order entry systems, the quantity ordered is captured and stored at the level of units a product per order received from the customers. Whenever you need summary data, you add up the individual transaction  

Usually the data In the data warehouse is not updated or deleted.

Figure 2-3 The data warehouse is nonvolatile,

If you are looking for units of a product ordered this month, you read all the Akers entered for the entire month for that product and add up. You do not usually keep summa-ry data in an operational system.

When a user queries the data ‘warehouse for analysis, he or she usually starts by looking at summary data. The user may start with total sale units of a product in an entire region. Then the user may want to look at the breakdown by states in the region. The next step may be the examination or sale units by the next level of individual stores. Frequently, the analysis begins at a high level and moves down to lower levels of detail.

In a data warehouse, therefore, you find it efficient to keep data summarized at different levels. Depending on the query, you can then go to the particular level of detail and satisfy the query. Data granularity in a data warehouse refers to the level of detail. The lower the level of detail, the finer the data granularity. Of course, if you want to keep data in the lowest level of detail, you have to store a lot of data in the data warehouse. You will have to decide on the granularity levels based on the data types and the expected system performance for queries. Figure 2-4 shows examples of data granularity in a typical data warehouse.