Data warehousing vs. data marts
Which should you build first: the data warehouse or the data mart? This is the question that has been bothering IT managers a lot lately. Most vendors would say that the data warehouses are difficult and expensive to do, and that they are not advisable. They say that the data warehouses take a long time to build. Also, they say that it faces a lot of issues concerning what the corporation is facing in the meantime. Some of the issues are the integration of legacy data, and the difficulty in managing big amounts of data. Data mart has definitely made a gloomy image out of the data warehouse, but these are all not true. A thorough definition and difference citing is needed for this misconception. But what are data marts and data warehouses?
First one must know that data mart represents a specific company. It represents its programs, data, software and hardware. It means there is separate data mart for each department. For example, there is a data mart for production, for finance, another for sales department, and another one for marketing. Every data mart has its own specific functions and features. It is not identical to other data marts from other departments, but they can coordinate together. Data mart is focused on individual and specific department, which is why it can’t handle big data. The star-join structure database is used to gather all data mart database for design. There are two kinds of data mart, the independent data mart (this is the stronger data) and the dependent data mart (this is the less stronger one). One must create multiple independent data marts so that it can be used for organization.
Data warehousing is broad and not limited to focusing only on specific departments. It can represent the entire company; it comprises all subjects and models of the corporate data. Data warehousing is not limited to being related to subject areas of departments and corporations. The data stored in data warehousing are more detailed compared to data mart. The way data warehousing index is light because it has to handle large volume of data. Data warehousing covers a large area of the corporation or company which is why it takes a long time to process it. That is also why data marts are quick and easy to use, design, and implement because it handles only small amounts of data. This is also why data warehousing is more expensive compared to data mart.
SUMMARY:
1.
Data Mart is focused on individual departments of the corporation or company while data warehousing can represent the entire company or corporation as a whole.
2.
Data mart can only process small amounts of data, unlike data warehousing that can process large amounts of data.
3.
Data warehousing can get expensive and difficult to use because it covers a broad part of the company or corporation, unlike the data mart which is affordable and convenient because it deals with small departments of the company or corporation.