Fundamental Themes For Your Data Warehouse
While each data warehouse may differ in their size, scope, or complexity, there are certain fundamental themes that they all share. The three important themes that all data warehouses share are processing time, drilling down, and drilling up. If a system has all of these factors, and it runs with a high level of efficiency, it can be truly called a data warehouse.
Despite the fact that all data warehouses are comprised of these three elements, they all lay the foundation for structures that are truly powerful. In this article, I will describe the fundamental themes that make up all data warehouses, and I will explain why they are so important.
The term "drilling down" is used to describe the addition of a row header, particularly within a relational database. In most cases, the row header will be added to a "select" statement. When a user studies the sales of a product at a specific level, such as from the manufacturer, the query will present them with information that is related to the sum, manufacturer, and sales. In addition to this, the query will also have information that is related to time or other units. If you want to drill down further to the brand that the manufacturers sale, you will want to add the appropriate row headers. Once this is done, the data warehouse will show numerous rows which list the brands which are sold.
A row header will often be referred to as being a grouping column. The reason for this is because all the items that are not connected to an operator such as SUM will need to be identified in the SGQ group with a certain clause. It could basically be said that grouping columns and row headers are identical. If the information contained in the example above is located in a dimensional star schema, the brand unit and the manufacturer unit can generally be found in the same dimension table. Once a user runs a query at the level of the manufacturers, they can see a collection of characteristics for the dimensions of the products. It is also possible for the user to place the attributes of a brand within the query.
Once this is done, the user can run a query again, and they can drill down in a certain manner. If the attributes for the manufacturer and brand are contained within the same dimensional table, then this reduces the number of adjustments that need to be made to the SQL. In other words, this will make the query much more simple. It is crucial for the data warehouse to support drilling down, especially at the user interface level. It is best to use a large amount of atomic data during this stage, and the reason for this is because the atomic data is much more dimensional than other forms of data. It should also be noted that the atomic data is more expressive as well.
When two points are combined together, the atomic data must be comprised of the same schema. The atomic data should be easily accessible, and this will make it easier when the drill down procedure is used. When a company fails to do this, it is one of the leading causes of having an architecture that is a strange structure. The atomic data may be hidden, and it may only be accessed after a user has used the drilling through process.
There are actual some people which support this structure, but most of them can never explain how this happens. These are generally people who have never used query tool that was designed by a commercial company.
There are a number of things a company should do if they wish to build a system for drilling down. They will first need to acquire and use query tools that are ad hoc. These should be tools that will showcase the drill down options without the need for distinct schema programming. Many experts have said that distinct schema programming is the bane of many data warehouses. The reason for this is because each schema will need to have an application that is custom built. This problem was quite prevalent during the 1990s, but this doesn’t mean that a company can’t fall victim to it today.