The Difference Between Data Mart and Data Warehouse
The biggest decision facing most IT managers today is whether or not they should construct the data mart before the data warehouse. Many vendors will tell you that data warehouses are hard to build, as well as expensive.
If you listen to some vendors, you may be left thinking that building data warehouses is a waste of time. However, this view is inaccurate, and any data mart vendor that tells you this are looking out for their own best interests.
The problem with many data mart vendors is that they see data warehouses as barriers which stop them from earning a profit. It is natural that they would tell you about all the disadvantages you will encounter when trying to build a data warehouse. Some vendors will even tell you that you can create a data warehouse by simply building a few data marts and allowing them to grow. However, there are a number of problems you will run into by using this method. When data warehouses were first advertised, data mart companies tried to tout their products as being the same product.
Unfortunately, many people were confused by this, especially those that went to trade shows. Many of these customers purchased data marts and begin constructing them without data warehouses. As they continued constructing data marts, they begin to realize that the architectural structure was flawed. There are a number of reasons why you will want to build data warehouse. If you only use data marts, the information between data marts will become redundant. The information that is presented from each data mart will be different, and this will demonstrate inconsistency.
People who use data marts will have a hard time managing the interface between them. Because of this, trying to use data marts in place of a data warehouse will not give your the results you are looking for. Despite these problems, many data mart companies are not willing to admit they made mistakes. Instead, they are now trying to say that data warehouses are merely a collection of data marts. Again, this is not correct, and will cause confusion among customers. There is not way you can purchase a collection of data marts and grow them into data warehouses.
It is also important to realize that data warehouses and data marts are not the same thing. There are some notable differences between the two. A data warehouse has a structure which is separate from a data mart, even though they may look similar. A data mart is a group of subjects that are organized in a way that allows them to assist departments in making specific decisions. For example, the advertising department will have its own data mart, while the finance department will have a data mart that is separate from it. In addition to this, each department will have full ownership of the software, hardware, and other components that make up their data mart.
Because of this, it is difficult to coordinate the data across multiple deparments. Each department will have its own view of how a data mart should look. The data mart that they use will be specific to them. In contrast, a data warehouse is designed around the organization as a whole. Instead of being owned by one department, a data warehouse will generally be owned by the entire company. While the data contained in data warehouses are granular, the information contained in data marts are not very granular at all.
Another thing that separates data warehouses from data marts is that data warehouses contain larger amounts of information. The information that is held by data marts are often summarized. Data warehouses will not contain information that is biased on the part of the department. Instead, it will demonstrate the information that is analyzed and processed by the organization.
Much of the information that is held in data warehouses is historical in nature, and they are designed to process this information. As you can see, there are many differences between data marts and data warehouses. It is important to make sure you’re not confused. Many people purchase data marts thinking that they are data warehouses, but this is not correct.
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